CubicGeodesicsHausdorffDist.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>.container { width:55% !important; }</style>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib notebook\n",
    "import matplotlib.pyplot as plt\n",
    "from lazy_imports import np\n",
    "from lazy_imports import plt\n",
    "from lazy_imports import sitk\n",
    "from lazy_imports import LinearOperator, gmres\n",
    "from lazy_imports import loadmat, savemat\n",
    "from lazy_imports import sitk\n",
    "import scipy.io as sio\n",
    "from scipy.spatial.distance import directed_hausdorff\n",
    "from IPython.core.display import display, HTML\n",
    "plt.rcParams[\"figure.figsize\"] = (4, 4) # (w, h)\n",
    "%matplotlib notebook\n",
    "display(HTML(\"<style>.container { width:55% !important; }</style>\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "from disp.vis import show_2d, show_2d_tensors\n",
    "from disp.vis import vis_tensors, vis_path, disp_scalar_to_file\n",
    "from disp.vis import disp_vector_to_file, disp_tensor_to_file\n",
    "from disp.vis import disp_gradG_to_file, disp_gradA_to_file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "from data.io import readRaw, ReadTensors, ReadScalars, WriteTensorNPArray\n",
    "from data.convert import GetNPArrayFromSITK, GetSITKImageFromNP\n",
    "import data.gen"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "from util import diff\n",
    "from util.diffeo import coord_register\n",
    "from util import tensors\n",
    "from util import maskops as mo\n",
    "from util.riemann import riem_vec_norm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "from util.tensors import scale_by_alpha"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "import algo.metricModSolver2d as mms\n",
    "import algo.geodesic as geo\n",
    "import algo.euler as euler\n",
    "import algo.dijkstra as dijkstra"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## I/O convention\n",
    "Due to the certain reason of Kris' simulated data generation, please follow the i/o convention below which is symmetric, to make sure the files are read and written correctly. Following example is for 2D situation, 3D case is analogous.\n",
    "### Read\n",
    "Shape of input_tensor.nhdr is [w, h, 3], and Shape of input_mask.nhdr is [w, h]\n",
    "```\n",
    "input_tensor = np.transpose(sitk.GetArrayFromImage(sitk.ReadImage(path)),(2,1,0))\n",
    "input_mask = np.transpose(sitk.GetArrayFromImage(sitk.ReadImage(path)),(1,0))\n",
    "```\n",
    "input_tensor.shape is [3, h, w], and input_mask.shape is [h, w]\n",
    "### Write\n",
    "output_tensor.shape is [3, h, w], and output_mask.shape is [h, w]\n",
    "```\n",
    "output_tensor = sitk.WriteImage(sitk.GetImageFromArray(np.transpose(output_tensor,(2,1,0)), path)\n",
    "output_mask = sitk.WriteImage(sitk.GetImageFromArray(np.transpose(output_tensor,(2,1,0)), path)\n",
    "```\n",
    "Shape of output_tensor.nhdr is [w, h, 3], and Shape of output_mask.nhdr is [w, h]\n",
    "\n",
    "## Caveat\n",
    "When using  `coord_register(geoy, geox, diffeo)`, remember to put `geoy` first, then goes the `geox`!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2D"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "path = '/home/sci/hdai/Projects/Atlas2D/Output/Cubic1246Atlas'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "def mean_of_min_dist(curve1, curve2):\n",
    "#     length = min(curve1.shape[0], curve2.shape[0])\n",
    "# #     dist_array = np.sqrt((curve1[:length,0]-curve2[:length,0])**2+(curve1[:length,1]-curve2[:length,1])**2)\n",
    "#     dist_array = np.sqrt((curve1[-length:,0]-curve2[-length:,0])**2+(curve1[-length:,1]-curve2[-length:,1])**2)\n",
    "    min_list = []\n",
    "    for i in range(curve1.shape[0]):\n",
    "        min_list.append(np.min(np.sqrt((curve2[:,0]-curve1[i,0])**2+(curve2[:,1]-curve1[i,1])**2)))\n",
    "#     return np.min(dist_array)\n",
    "    return np.mean(np.array(min_list))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "# geox_list, geoy_list = [],[]\n",
    "\n",
    "# ten_4_path = np.transpose(sitk.GetArrayFromImage(sitk.ReadImage(f'{path}/atlas_40_tens.nhdr')),(2,1,0))\n",
    "# filt_mask = np.transpose(sitk.GetArrayFromImage(sitk.ReadImage(f'{path}/atlas_0_mask.nhdr')),(1,0))\n",
    "# mat_4_path = np.zeros((100,100,2,2))\n",
    "# mat_4_path[:,:,0,0] = ten_4_path[0]\n",
    "# mat_4_path[:,:,0,1] = ten_4_path[1]\n",
    "# mat_4_path[:,:,1,0] = ten_4_path[1]\n",
    "# mat_4_path[:,:,1,1] = ten_4_path[2]\n",
    "# invmat_4_path = np.linalg.inv(mat_4_path)\n",
    "# invten_4_path = np.zeros((3,100,100))\n",
    "# invten_4_path[0] = invmat_4_path[:,:,0,0]\n",
    "# invten_4_path[1] = invmat_4_path[:,:,0,1]\n",
    "# invten_4_path[2] = invmat_4_path[:,:,1,1]\n",
    "\n",
    "# start_coords = [[13, 14]]\n",
    "# init_velocities = [None]\n",
    "# t1_file = None\n",
    "# tens_scale = 1e-1\n",
    "# geo_delta_t = 0.1\n",
    "# geo_iters = 60000\n",
    "# geox, geoy = geo.geodesicpath(ten_4_path, filt_mask,\\\n",
    "#                                   start_coords[0], init_velocities[0], \\\n",
    "#                                   geo_delta_t, iter_num=geo_iters, both_directions=False)\n",
    "    \n",
    "# geox_list.append(geox)\n",
    "# geoy_list.append(geoy)\n",
    "# # tens_fig = vis_tensors(invten_4_path*np.stack((filt_mask,filt_mask,filt_mask),0), f'cubic1', False, scale=5e2)\n",
    "# tens_fig = vis_tensors(ten_4_path*np.stack((filt_mask,filt_mask,filt_mask),0), f'cubic1', False, scale=5e-4)\n",
    "# vis_path(geox, geoy, tens_fig, \"Geodesic Original Tensors\", 'tab:blue', 80, 1, False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Geodesic on cubic5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Finding geodesic path from [13, 14] with initial velocity [0.27513762 0.96140485]\n"
     ]
    }
   ],
   "source": [
    "ten_4_path = np.transpose(sitk.GetArrayFromImage(sitk.ReadImage(f'Data/Cubic/cubic5_scaled_tensors.nhdr')),(2,1,0))\n",
    "filt_mask = np.transpose(sitk.GetArrayFromImage(sitk.ReadImage(f'Data/Cubic/cubic5_filt_mask.nhdr')),(1,0))\n",
    "\n",
    "start_coords = [[13, 14]]\n",
    "init_velocities = [None]\n",
    "t1_file = None\n",
    "tens_scale = 1e-1\n",
    "geo_delta_t = 0.1\n",
    "geo_iters = 60000\n",
    "gt_geox, gt_geoy = geo.geodesicpath(ten_4_path, filt_mask,\\\n",
    "                                      start_coords[0], init_velocities[0], \\\n",
    "                                      geo_delta_t, iter_num=geo_iters, both_directions=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Geodesic on atlas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Finding geodesic path from [13, 14] with initial velocity [0.27003975 0.96284917]\n"
     ]
    }
   ],
   "source": [
    "ten_4_path = np.transpose(sitk.GetArrayFromImage(sitk.ReadImage(f'Output/Cubic1246Atlas/atlas_100_tens.nhdr')),(2,1,0))\n",
    "filt_mask = np.transpose(sitk.GetArrayFromImage(sitk.ReadImage(f'Data/Cubic/cubic5_filt_mask.nhdr')),(1,0))\n",
    "\n",
    "start_coords = [[13, 14]]\n",
    "init_velocities = [None]\n",
    "t1_file = None\n",
    "tens_scale = 1e-1\n",
    "geo_delta_t = 0.1\n",
    "geo_iters = 60000\n",
    "atlas_geox, atlas_geoy = geo.geodesicpath(ten_4_path, filt_mask,\\\n",
    "                                      start_coords[0], init_velocities[0], \\\n",
    "                                      geo_delta_t, iter_num=geo_iters, both_directions=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Hausdorff between registered geodesic on cubic1 and geodesic on atlas/cubic5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Finding geodesic path from [13, 14] with initial velocity [0.32249473 0.94657126]\n",
      "2.8967245288010317\n",
      "1.7777036370661263\n",
      "1.1553437793410324\n",
      "0.863958077400405\n",
      "0.7199549575086014\n",
      "0.639773585335763\n",
      "0.5984786984413406\n",
      "0.584708274486787\n",
      "0.5781365794734926\n",
      "0.5735681624103193\n",
      "0.569603569580335\n",
      "0.5659259676568427\n",
      "0.5622095428229266\n",
      "0.558676386369756\n",
      "0.5553129476230967\n",
      "0.5520904257515897\n",
      "0.5489715256846104\n",
      "0.5459597122891053\n",
      "0.5431552506638709\n",
      "0.5405066526178168\n",
      "0.5379396057386839\n"
     ]
    }
   ],
   "source": [
    "cubic1_dist = []\n",
    "cubic_num = 1\n",
    "ten_4_path = np.transpose(sitk.GetArrayFromImage(sitk.ReadImage(f'Data/Cubic/cubic{cubic_num}_scaled_tensors.nhdr')),(2,1,0))\n",
    "filt_mask = np.transpose(sitk.GetArrayFromImage(sitk.ReadImage(f'Data/Cubic/cubic{cubic_num}_filt_mask.nhdr')),(1,0))\n",
    "\n",
    "start_coords = [[13, 14]]\n",
    "init_velocities = [None]\n",
    "t1_file = None\n",
    "tens_scale = 1e-1\n",
    "geo_delta_t = 0.1\n",
    "geo_iters = 60000\n",
    "geox, geoy = geo.geodesicpath(ten_4_path, filt_mask,\\\n",
    "                                      start_coords[0], init_velocities[0], \\\n",
    "                                      geo_delta_t, iter_num=geo_iters, both_directions=False)\n",
    "    \n",
    "for i in range(0,101,5):\n",
    "# if True:\n",
    "#     i=80\n",
    "    diffeo = sio.loadmat(f'Output/Cubic1246Atlas/cubic{cubic_num}_{i}_phi.mat')['diffeo']\n",
    "    new_geoy, new_geox = coord_register(geoy, geox, diffeo)\n",
    "#     tens_fig = vis_tensors(ten_4_path, f'cubic_12_geodesic_{i}', False, scale=tens_scale)\n",
    "# #     vis_path(geox, geoy, tens_fig, \"cubic\", 'tab:blue', 40, 1, False)\n",
    "#     vis_path(new_geox[800:802], new_geoy[800:802], tens_fig, \"registered\", 'tab:red', 40, 1, False)\n",
    "# #     vis_path(gt_geox, gt_geoy, tens_fig, \"groundtruth\", 'tab:green', 40, 1, False)\n",
    "#     vis_path(atlas_geox[900:902], atlas_geoy[900:902], tens_fig, \"atlas\", 'tab:orange', 40, 1, False)\n",
    "    \n",
    "    geo_subject_registered = np.concatenate((np.expand_dims(np.array(new_geox),axis=1),np.expand_dims(np.array(new_geoy),axis=1)),axis=1)\n",
    "    geo_gt = np.concatenate((np.expand_dims(np.array(gt_geox),axis=1),np.expand_dims(np.array(gt_geoy),axis=1)),axis=1)\n",
    "    geo_atlas = np.concatenate((np.expand_dims(np.array(atlas_geox),axis=1),np.expand_dims(np.array(atlas_geoy),axis=1)),axis=1)\n",
    "#     dist_hausdorff = directed_hausdorff(geo_subject_registered, geo_atlas)[0]\n",
    "#     cubic1_dist.append(dist_hausdorff)\n",
    "#     print(dist_hausdorff)\n",
    "    dist_mean = mean_of_min_dist(geo_subject_registered, geo_atlas)\n",
    "    cubic1_dist.append(dist_mean)\n",
    "    print(dist_mean)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "tens_list = []\n",
    "tens_list.append(ten_4_path)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Hausdorff between registered geodesic on cubic2 and geodesic on atlas/cubic5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Finding geodesic path from [13, 14] with initial velocity [0.22961718 0.97328102]\n",
      "3.318115831685355\n",
      "2.2872426630750002\n",
      "1.7149908129612992\n",
      "1.3787415339791478\n",
      "1.1736626487002706\n",
      "1.0428475084189812\n",
      "0.955986766086406\n",
      "0.8960495230394776\n",
      "0.8531501258193881\n",
      "0.8212855031497243\n",
      "0.7968339506677357\n",
      "0.777480035566518\n",
      "0.7616493557955851\n",
      "0.74837896499253\n",
      "0.7370910045449421\n",
      "0.7272657803335846\n",
      "0.7185401084728518\n",
      "0.7107492315769366\n",
      "0.7037827946212898\n",
      "0.6974483151427155\n",
      "0.6916302661028711\n"
     ]
    }
   ],
   "source": [
    "cubic2_dist = []\n",
    "cubic_num = 2\n",
    "ten_4_path = np.transpose(sitk.GetArrayFromImage(sitk.ReadImage(f'Data/Cubic/cubic{cubic_num}_scaled_tensors.nhdr')),(2,1,0))\n",
    "filt_mask = np.transpose(sitk.GetArrayFromImage(sitk.ReadImage(f'Data/Cubic/cubic{cubic_num}_filt_mask.nhdr')),(1,0))\n",
    "\n",
    "start_coords = [[13, 14]]\n",
    "init_velocities = [None]\n",
    "t1_file = None\n",
    "tens_scale = 1e-1\n",
    "geo_delta_t = 0.1\n",
    "geo_iters = 60000\n",
    "geox, geoy = geo.geodesicpath(ten_4_path, filt_mask,\\\n",
    "                                      start_coords[0], init_velocities[0], \\\n",
    "                                      geo_delta_t, iter_num=geo_iters, both_directions=False)\n",
    "    \n",
    "for i in range(0,101,5):\n",
    "    diffeo = sio.loadmat(f'Output/Cubic1246Atlas/cubic{cubic_num}_{i}_phi.mat')['diffeo']\n",
    "    new_geoy, new_geox = coord_register(geoy, geox, diffeo)\n",
    "#     tens_fig = vis_tensors(ten_4_path, f'cubic_12_geodesic_{i}', False, scale=tens_scale)\n",
    "#     vis_path(geox, geoy, tens_fig, \"cubic\", 'tab:blue', 40, 1, False)\n",
    "#     vis_path(new_geox, new_geoy, tens_fig, \"registered\", 'tab:red', 40, 1, False)\n",
    "#     vis_path(gt_geox, gt_geoy, tens_fig, \"groundtruth\", 'tab:green', 40, 1, False)\n",
    "#     vis_path(atlas_geox, atlas_geoy, tens_fig, \"atlas\", 'tab:orange', 40, 1, False)\n",
    "    \n",
    "    geo_subject_registered = np.concatenate((np.expand_dims(np.array(new_geox),axis=1),np.expand_dims(np.array(new_geoy),axis=1)),axis=1)\n",
    "    geo_gt = np.concatenate((np.expand_dims(np.array(gt_geox),axis=1),np.expand_dims(np.array(gt_geoy),axis=1)),axis=1)\n",
    "    geo_atlas = np.concatenate((np.expand_dims(np.array(atlas_geox),axis=1),np.expand_dims(np.array(atlas_geoy),axis=1)),axis=1)\n",
    "#     dist_hausdorff = directed_hausdorff(geo_subject_registered, geo_atlas)[0]\n",
    "#     cubic2_dist.append(dist_hausdorff)\n",
    "#     print(dist_hausdorff)\n",
    "    dist_mean = mean_of_min_dist(geo_subject_registered, geo_atlas)\n",
    "    cubic2_dist.append(dist_mean)\n",
    "    print(dist_mean)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "tens_list.append(ten_4_path)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Hausdorff between registered geodesic on cubic4 and geodesic on atlas/cubic5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Finding geodesic path from [13, 14] with initial velocity [0.26587849 0.96400655]\n",
      "0.8488663452409569\n",
      "0.6141317328793943\n",
      "0.49648439102504965\n",
      "0.4535538282311163\n",
      "0.4412415794507539\n",
      "0.43487949466331305\n",
      "0.4314390894990118\n",
      "0.42975491376534\n",
      "0.4288771888694543\n",
      "0.4283338885535385\n",
      "0.4279740245902054\n",
      "0.42773746271405416\n",
      "0.4275914977503837\n",
      "0.4275142992701475\n",
      "0.42749213659966423\n",
      "0.4275087391770165\n",
      "0.4275682857468648\n",
      "0.4276525977914939\n",
      "0.42776446004647484\n",
      "0.4278927951088438\n",
      "0.42802813546311264\n"
     ]
    }
   ],
   "source": [
    "cubic4_dist = []\n",
    "cubic_num = 4\n",
    "ten_4_path = np.transpose(sitk.GetArrayFromImage(sitk.ReadImage(f'Data/Cubic/cubic{cubic_num}_scaled_tensors.nhdr')),(2,1,0))\n",
    "filt_mask = np.transpose(sitk.GetArrayFromImage(sitk.ReadImage(f'Data/Cubic/cubic{cubic_num}_filt_mask.nhdr')),(1,0))\n",
    "\n",
    "start_coords = [[13, 14]]\n",
    "init_velocities = [None]\n",
    "t1_file = None\n",
    "tens_scale = 1e-1\n",
    "geo_delta_t = 0.1\n",
    "geo_iters = 60000\n",
    "geox, geoy = geo.geodesicpath(ten_4_path, filt_mask,\\\n",
    "                                      start_coords[0], init_velocities[0], \\\n",
    "                                      geo_delta_t, iter_num=geo_iters, both_directions=False)\n",
    "    \n",
    "for i in range(0,101,5):\n",
    "    diffeo = sio.loadmat(f'Output/Cubic1246Atlas/cubic{cubic_num}_{i}_phi.mat')['diffeo']\n",
    "    new_geoy, new_geox = coord_register(geoy, geox, diffeo)\n",
    "#     tens_fig = vis_tensors(ten_4_path, f'cubic_12_geodesic_{i}', False, scale=tens_scale)\n",
    "#     vis_path(geox, geoy, tens_fig, \"cubic\", 'tab:blue', 40, 1, False)\n",
    "#     vis_path(new_geox, new_geoy, tens_fig, \"registered\", 'tab:red', 40, 1, False)\n",
    "#     vis_path(gt_geox, gt_geoy, tens_fig, \"groundtruth\", 'tab:green', 40, 1, False)\n",
    "#     vis_path(atlas_geox, atlas_geoy, tens_fig, \"atlas\", 'tab:orange', 40, 1, False)\n",
    "    \n",
    "    geo_subject_registered = np.concatenate((np.expand_dims(np.array(new_geox),axis=1),np.expand_dims(np.array(new_geoy),axis=1)),axis=1)\n",
    "    geo_gt = np.concatenate((np.expand_dims(np.array(gt_geox),axis=1),np.expand_dims(np.array(gt_geoy),axis=1)),axis=1)\n",
    "    geo_atlas = np.concatenate((np.expand_dims(np.array(atlas_geox),axis=1),np.expand_dims(np.array(atlas_geoy),axis=1)),axis=1)\n",
    "#     dist_hausdorff = directed_hausdorff(geo_subject_registered, geo_atlas)[0]\n",
    "#     cubic4_dist.append(dist_hausdorff)\n",
    "#     print(dist_hausdorff)\n",
    "\n",
    "    dist_mean = mean_of_min_dist(geo_subject_registered, geo_atlas)\n",
    "    cubic4_dist.append(dist_mean)\n",
    "    print(dist_mean)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "tens_list.append(ten_4_path)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Hausdorff between registered geodesic on cubic6 and geodesic on atlas/cubic5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Finding geodesic path from [13, 14] with initial velocity [0.28446983 0.95868499]\n",
      "0.7743015689516707\n",
      "0.6393359800648324\n",
      "0.5875908707262048\n",
      "0.5602971884613235\n",
      "0.5447120923374803\n",
      "0.5338206414447664\n",
      "0.5261711659042958\n",
      "0.5212320452982303\n",
      "0.518104327724401\n",
      "0.5158785615170596\n",
      "0.5145525003757587\n",
      "0.5139053883834483\n",
      "0.5137064676442806\n",
      "0.5137323258944978\n",
      "0.5138661509831304\n",
      "0.5140264970591261\n",
      "0.5141902756769796\n",
      "0.5143172862806276\n",
      "0.514509080790415\n",
      "0.5147533321274168\n",
      "0.5150242583873238\n"
     ]
    }
   ],
   "source": [
    "cubic6_dist = []\n",
    "cubic_num = 6\n",
    "ten_4_path = np.transpose(sitk.GetArrayFromImage(sitk.ReadImage(f'Data/Cubic/cubic{cubic_num}_scaled_tensors.nhdr')),(2,1,0))\n",
    "filt_mask = np.transpose(sitk.GetArrayFromImage(sitk.ReadImage(f'Data/Cubic/cubic{cubic_num}_filt_mask.nhdr')),(1,0))\n",
    "\n",
    "start_coords = [[13, 14]]\n",
    "init_velocities = [None]\n",
    "t1_file = None\n",
    "tens_scale = 1e-1\n",
    "geo_delta_t = 0.1\n",
    "geo_iters = 60000\n",
    "geox, geoy = geo.geodesicpath(ten_4_path, filt_mask,\\\n",
    "                                      start_coords[0], init_velocities[0], \\\n",
    "                                      geo_delta_t, iter_num=geo_iters, both_directions=False)\n",
    "    \n",
    "for i in range(0,101,5):\n",
    "    diffeo = sio.loadmat(f'Output/Cubic1246Atlas/cubic{cubic_num}_{i}_phi.mat')['diffeo']\n",
    "    new_geoy, new_geox = coord_register(geoy, geox, diffeo)\n",
    "#     tens_fig = vis_tensors(ten_4_path, f'cubic_12_geodesic_{i}', False, scale=tens_scale)\n",
    "#     vis_path(geox, geoy, tens_fig, \"cubic\", 'tab:blue', 40, 1, False)\n",
    "#     vis_path(new_geox, new_geoy, tens_fig, \"registered\", 'tab:red', 40, 1, False)\n",
    "#     vis_path(gt_geox, gt_geoy, tens_fig, \"groundtruth\", 'tab:green', 40, 1, False)\n",
    "#     vis_path(atlas_geox, atlas_geoy, tens_fig, \"atlas\", 'tab:orange', 40, 1, False)\n",
    "    \n",
    "    geo_subject_registered = np.concatenate((np.expand_dims(np.array(new_geox),axis=1),np.expand_dims(np.array(new_geoy),axis=1)),axis=1)\n",
    "    geo_gt = np.concatenate((np.expand_dims(np.array(gt_geox),axis=1),np.expand_dims(np.array(gt_geoy),axis=1)),axis=1)\n",
    "    geo_atlas = np.concatenate((np.expand_dims(np.array(atlas_geox),axis=1),np.expand_dims(np.array(atlas_geoy),axis=1)),axis=1)\n",
    "#     dist_hausdorff = directed_hausdorff(geo_subject_registered, geo_atlas)[0]\n",
    "#     cubic6_dist.append(dist_hausdorff)\n",
    "#     print(dist_hausdorff)\n",
    "\n",
    "    dist_mean = mean_of_min_dist(geo_subject_registered, geo_atlas)\n",
    "    cubic6_dist.append(dist_mean)\n",
    "    print(dist_mean)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "tens_list.append(ten_4_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# plt.figure(figsize=(8,4))\n",
    "# plt.plot(list(range(0,101,5)),cubic1_dist, label='cubic 1')\n",
    "# plt.plot(list(range(0,101,5)),cubic2_dist, label='cubic 2')\n",
    "# plt.plot(list(range(0,101,5)),cubic4_dist, label='cubic 4')\n",
    "# plt.plot(list(range(0,101,5)),cubic6_dist, label='cubic 6')\n",
    "# plt.legend(loc='upper right', ncol=1)\n",
    "# plt.title(f'Hausdorff distance between the geodesics on subjects and atlas')\n",
    "# plt.xlabel('Iterations')\n",
    "# plt.ylabel('Hausdorff distance')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "interp_colors = ['#777777','#253494', '#2c7fb8', '#41b6c4', '#a1dab4', '#fed98e', '#fe9929', '#d95f0e', '#993404']\n",
    "\n",
    "# use first 4 colors in interp_colors for metrics 1-4\n",
    "\n",
    "# matches defaults\n",
    "XSMALL_SIZE = 12\n",
    "SMALL_SIZE = 15\n",
    "MEDIUM_SIZE = 18\n",
    "BIGGER_SIZE = 20\n",
    "# bigger for papers\n",
    "XSMALL_SIZE = 12\n",
    "SMALL_SIZE = 17\n",
    "MEDIUM_SIZE = 23\n",
    "BIGGER_SIZE = 25\n",
    "\n",
    "plt.rcParams[\"figure.figsize\"] = (4, 4) # (w, h)\n",
    "plt.rc('font', size=SMALL_SIZE)          # controls default text sizes\n",
    "plt.rc('axes', titlesize=SMALL_SIZE)     # fontsize of the axes title\n",
    "plt.rc('axes', labelsize=MEDIUM_SIZE)    # fontsize of the x and y labels\n",
    "plt.rc('xtick', labelsize=SMALL_SIZE)    # fontsize of the tick labels\n",
    "plt.rc('ytick', labelsize=SMALL_SIZE)    # fontsize of the tick labels\n",
    "plt.rc('legend', fontsize=SMALL_SIZE)    # legend fontsize\n",
    "plt.rc('figure', titlesize=BIGGER_SIZE)  # fontsize of the figure title\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
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       "    status_bar.classList = 'mpl-message';\n",
       "    toolbar.appendChild(status_bar);\n",
       "    this.message = status_bar;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.request_resize = function (x_pixels, y_pixels) {\n",
       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
       "    // which will in turn request a refresh of the image.\n",
       "    this.send_message('resize', { width: x_pixels, height: y_pixels });\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.send_message = function (type, properties) {\n",
       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.send_draw_message = function () {\n",
       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
       "        this.ws.send(JSON.stringify({ type: 'draw', figure_id: this.id }));\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_save = function (fig, _msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function (fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] !== fig.canvas.width || size[1] !== fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1], msg['forward']);\n",
       "        fig.send_message('refresh', {});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function (fig, msg) {\n",
       "    var x0 = msg['x0'] / mpl.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
       "    var x1 = msg['x1'] / mpl.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0,\n",
       "        0,\n",
       "        fig.canvas.width / mpl.ratio,\n",
       "        fig.canvas.height / mpl.ratio\n",
       "    );\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function (fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function (fig, msg) {\n",
       "    var cursor = msg['cursor'];\n",
       "    switch (cursor) {\n",
       "        case 0:\n",
       "            cursor = 'pointer';\n",
       "            break;\n",
       "        case 1:\n",
       "            cursor = 'default';\n",
       "            break;\n",
       "        case 2:\n",
       "            cursor = 'crosshair';\n",
       "            break;\n",
       "        case 3:\n",
       "            cursor = 'move';\n",
       "            break;\n",
       "    }\n",
       "    fig.rubberband_canvas.style.cursor = cursor;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_message = function (fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function (fig, _msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function (fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_history_buttons = function (fig, msg) {\n",
       "    for (var key in msg) {\n",
       "        if (!(key in fig.buttons)) {\n",
       "            continue;\n",
       "        }\n",
       "        fig.buttons[key].disabled = !msg[key];\n",
       "        fig.buttons[key].setAttribute('aria-disabled', !msg[key]);\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_navigate_mode = function (fig, msg) {\n",
       "    if (msg['mode'] === 'PAN') {\n",
       "        fig.buttons['Pan'].classList.add('active');\n",
       "        fig.buttons['Zoom'].classList.remove('active');\n",
       "    } else if (msg['mode'] === 'ZOOM') {\n",
       "        fig.buttons['Pan'].classList.remove('active');\n",
       "        fig.buttons['Zoom'].classList.add('active');\n",
       "    } else {\n",
       "        fig.buttons['Pan'].classList.remove('active');\n",
       "        fig.buttons['Zoom'].classList.remove('active');\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function () {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message('ack', {});\n",
       "};\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function (fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            /* FIXME: We get \"Resource interpreted as Image but\n",
       "             * transferred with MIME type text/plain:\" errors on\n",
       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "             * to be part of the websocket stream */\n",
       "            evt.data.type = 'image/png';\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src\n",
       "                );\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data\n",
       "            );\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        } else if (\n",
       "            typeof evt.data === 'string' &&\n",
       "            evt.data.slice(0, 21) === 'data:image/png;base64'\n",
       "        ) {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig['handle_' + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\n",
       "                \"No handler for the '\" + msg_type + \"' message type: \",\n",
       "                msg\n",
       "            );\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\n",
       "                    \"Exception inside the 'handler_\" + msg_type + \"' callback:\",\n",
       "                    e,\n",
       "                    e.stack,\n",
       "                    msg\n",
       "                );\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "};\n",
       "\n",
       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function (e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e) {\n",
       "        e = window.event;\n",
       "    }\n",
       "    if (e.target) {\n",
       "        targ = e.target;\n",
       "    } else if (e.srcElement) {\n",
       "        targ = e.srcElement;\n",
       "    }\n",
       "    if (targ.nodeType === 3) {\n",
       "        // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "    }\n",
       "\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    var boundingRect = targ.getBoundingClientRect();\n",
       "    var x = e.pageX - (boundingRect.left + document.body.scrollLeft);\n",
       "    var y = e.pageY - (boundingRect.top + document.body.scrollTop);\n",
       "\n",
       "    return { x: x, y: y };\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * http://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys(original) {\n",
       "    return Object.keys(original).reduce(function (obj, key) {\n",
       "        if (typeof original[key] !== 'object') {\n",
       "            obj[key] = original[key];\n",
       "        }\n",
       "        return obj;\n",
       "    }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function (event, name) {\n",
       "    var canvas_pos = mpl.findpos(event);\n",
       "\n",
       "    if (name === 'button_press') {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * mpl.ratio;\n",
       "    var y = canvas_pos.y * mpl.ratio;\n",
       "\n",
       "    this.send_message(name, {\n",
       "        x: x,\n",
       "        y: y,\n",
       "        button: event.button,\n",
       "        step: event.step,\n",
       "        guiEvent: simpleKeys(event),\n",
       "    });\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function (_event, _name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.key_event = function (event, name) {\n",
       "    // Prevent repeat events\n",
       "    if (name === 'key_press') {\n",
       "        if (event.which === this._key) {\n",
       "            return;\n",
       "        } else {\n",
       "            this._key = event.which;\n",
       "        }\n",
       "    }\n",
       "    if (name === 'key_release') {\n",
       "        this._key = null;\n",
       "    }\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which !== 17) {\n",
       "        value += 'ctrl+';\n",
       "    }\n",
       "    if (event.altKey && event.which !== 18) {\n",
       "        value += 'alt+';\n",
       "    }\n",
       "    if (event.shiftKey && event.which !== 16) {\n",
       "        value += 'shift+';\n",
       "    }\n",
       "\n",
       "    value += 'k';\n",
       "    value += event.which.toString();\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, { key: value, guiEvent: simpleKeys(event) });\n",
       "    return false;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function (name) {\n",
       "    if (name === 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message('toolbar_button', { name: name });\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function (tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Left button pans, Right button zooms\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
       "\n",
       "mpl.default_extension = \"png\";/* global mpl */\n",
       "\n",
       "var comm_websocket_adapter = function (comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.close = function () {\n",
       "        comm.close();\n",
       "    };\n",
       "    ws.send = function (m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function (msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data']);\n",
       "    });\n",
       "    return ws;\n",
       "};\n",
       "\n",
       "mpl.mpl_figure_comm = function (comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = document.getElementById(id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm);\n",
       "\n",
       "    function ondownload(figure, _format) {\n",
       "        window.open(figure.canvas.toDataURL());\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy, ondownload, element);\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element;\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error('Failed to find cell for figure', id, fig);\n",
       "        return;\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function (fig, msg) {\n",
       "    var width = fig.canvas.width / mpl.ratio;\n",
       "    fig.root.removeEventListener('remove', this._remove_fig_handler);\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable();\n",
       "    fig.parent_element.innerHTML =\n",
       "        '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "    fig.close_ws(fig, msg);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.close_ws = function (fig, msg) {\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function (_remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var width = this.canvas.width / mpl.ratio;\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] =\n",
       "        '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function () {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message('ack', {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () {\n",
       "        fig.push_to_output();\n",
       "    }, 1000);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function () {\n",
       "    var fig = this;\n",
       "\n",
       "    var toolbar = document.createElement('div');\n",
       "    toolbar.classList = 'btn-toolbar';\n",
       "    this.root.appendChild(toolbar);\n",
       "\n",
       "    function on_click_closure(name) {\n",
       "        return function (_event) {\n",
       "            return fig.toolbar_button_onclick(name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    function on_mouseover_closure(tooltip) {\n",
       "        return function (event) {\n",
       "            if (!event.currentTarget.disabled) {\n",
       "                return fig.toolbar_button_onmouseover(tooltip);\n",
       "            }\n",
       "        };\n",
       "    }\n",
       "\n",
       "    fig.buttons = {};\n",
       "    var buttonGroup = document.createElement('div');\n",
       "    buttonGroup.classList = 'btn-group';\n",
       "    var button;\n",
       "    for (var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            /* Instead of a spacer, we start a new button group. */\n",
       "            if (buttonGroup.hasChildNodes()) {\n",
       "                toolbar.appendChild(buttonGroup);\n",
       "            }\n",
       "            buttonGroup = document.createElement('div');\n",
       "            buttonGroup.classList = 'btn-group';\n",
       "            continue;\n",
       "        }\n",
       "\n",
       "        button = fig.buttons[name] = document.createElement('button');\n",
       "        button.classList = 'btn btn-default';\n",
       "        button.href = '#';\n",
       "        button.title = name;\n",
       "        button.innerHTML = '<i class=\"fa ' + image + ' fa-lg\"></i>';\n",
       "        button.addEventListener('click', on_click_closure(method_name));\n",
       "        button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n",
       "        buttonGroup.appendChild(button);\n",
       "    }\n",
       "\n",
       "    if (buttonGroup.hasChildNodes()) {\n",
       "        toolbar.appendChild(buttonGroup);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = document.createElement('span');\n",
       "    status_bar.classList = 'mpl-message pull-right';\n",
       "    toolbar.appendChild(status_bar);\n",
       "    this.message = status_bar;\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = document.createElement('div');\n",
       "    buttongrp.classList = 'btn-group inline pull-right';\n",
       "    button = document.createElement('button');\n",
       "    button.classList = 'btn btn-mini btn-primary';\n",
       "    button.href = '#';\n",
       "    button.title = 'Stop Interaction';\n",
       "    button.innerHTML = '<i class=\"fa fa-power-off icon-remove icon-large\"></i>';\n",
       "    button.addEventListener('click', function (_evt) {\n",
       "        fig.handle_close(fig, {});\n",
       "    });\n",
       "    button.addEventListener(\n",
       "        'mouseover',\n",
       "        on_mouseover_closure('Stop Interaction')\n",
       "    );\n",
       "    buttongrp.appendChild(button);\n",
       "    var titlebar = this.root.querySelector('.ui-dialog-titlebar');\n",
       "    titlebar.insertBefore(buttongrp, titlebar.firstChild);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._remove_fig_handler = function () {\n",
       "    this.close_ws(this, {});\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function (el) {\n",
       "    el.style.boxSizing = 'content-box'; // override notebook setting of border-box.\n",
       "    el.addEventListener('remove', this._remove_fig_handler);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function (el) {\n",
       "    // this is important to make the div 'focusable\n",
       "    el.setAttribute('tabindex', 0);\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    } else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function (event, _name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager) {\n",
       "        manager = IPython.keyboard_manager;\n",
       "    }\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which === 13) {\n",
       "        this.canvas_div.blur();\n",
       "        // select the cell after this one\n",
       "        var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
       "        IPython.notebook.select(index + 1);\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_save = function (fig, _msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "};\n",
       "\n",
       "mpl.find_output_cell = function (html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i = 0; i < ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code') {\n",
       "            for (var j = 0; j < cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] === html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "};\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel !== null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target(\n",
       "        'matplotlib',\n",
       "        mpl.mpl_figure_comm\n",
       "    );\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<img 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\" width=\"1200\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,9))\n",
    "plt.plot(list(range(0,101,5)),cubic1_dist, label='metric 1', color=interp_colors[0])\n",
    "plt.plot(list(range(0,101,5)),cubic2_dist, label='metric 2', color=interp_colors[1])\n",
    "plt.plot(list(range(0,101,5)),cubic4_dist, label='metric 3', color=interp_colors[2])\n",
    "plt.plot(list(range(0,101,5)),cubic6_dist, label='metric 4', color=interp_colors[3])\n",
    "plt.legend(loc='upper right', ncol=1)\n",
    "# plt.title(f'Mean distance between the geodesics on subjects and atlas')\n",
    "plt.xlabel('Iterations')\n",
    "plt.ylabel('Mean distance')\n",
    "plt.savefig('dist_plot.pdf')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "import random\n",
    "import torch\n",
    "random.shuffle(tens_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "20497.03420451364"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sum(tens_list[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "a=list(range(6))\n",
    "b=a.copy()\n",
    "random.shuffle(a)\n",
    "c=dict(zip(b,a))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{0: 2, 1: 5, 2: 4, 3: 1, 4: 3, 5: 0}"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.15"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}