{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Tubeness Filter (WIP: has bugs, descriptions not complete)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Added new repo: scijava.public\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "method": "display_data"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "9f16381e-31cc-48ec-ae2a-8fa2a97d67b9",
       "version_major": 2,
       "version_minor": 0
      },
      "method": "display_data"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "net.imagej.ImageJ@46956d8a"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "//load ImageJ\n",
    "%classpath config resolver scijava.public https://maven.scijava.org/content/groups/public\n",
    "%classpath add mvn net.imagej imagej 2.0.0-rc-67\n",
    "\n",
    "//create ImageJ object\n",
    "ij = new net.imagej.ImageJ()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This `Op` does ...."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Available operations:\n",
       "\t(IterableInterval out?) =\n",
       "\tnet.imagej.ops.filter.tubeness.DefaultTubeness(\n",
       "\t\tIterableInterval out?,\n",
       "\t\tRandomAccessibleInterval in,\n",
       "\t\tdouble sigma,\n",
       "\t\tdouble[] calibration)"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ij.op().help(\"tubeness\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is how the Op works..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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eMFxSGHXmrdUFY2JjI1BXn7Zb1mOu6PqT1RWgjLtD1ON04shYpaBqwlh7f1iR4TBM8yQrFBNVhUxhIF7dcwVWy7Q6VL8ajNtwqjK+CuJzZ6TIyg4buu2n51VDfdDV+tYpt+pzz9vwhypia3ohhVU9Ua3CWcgKSlMyQ7f9JMrUTsi+qklmhZ14wuuHPAHUP6t07rpQlCzDZVYSxyiQDTAU5mH55XdmDVQb4KhZ8dghf8fzzz9//T0em3ZWjjjmSrkN3faziZe1HA3zYjjU38j/D20Flh6O/8TYcmNWi1SLk0np3U+cs5ZEjaddDgOjpKvj8r5DlwzUsNV+54Hod+Ria97MKJAVg3DOXVWLEUTFWFUTs0adRI5Rh7KcO7HsrVTNQIG4od39XIa5JVMUy5gTK5Rw0ZLiGL/Hd+W+Wo48zT/ud1EyXGpDv7sjr0KTJUtmaN5PHSiOkflWHVlNNVWy54mgOIqTrWCswzDO43zgFAZBURVp6X1VidZm4JgKbdlHqyrXqtk7oQz97hdELRvK5q3XWe0sqtVxGjgWH3bq9XvQHI1rH15PpqlOvzuWscVqPxgOnUCdAAqiH1P+MB4qWF22tGlcdu2RRoGstJFaWU58zZ47BVAmDX46dmO7qlQ7WputjPGXbeVLMR0a3k1BE9BoS1gs53eUDdUGPrZLLeOgVioC+J4LpFczMjS8e8yrbsfinbUeqmuu/W/P22fH3u8MBQoM/zV3pCFjZIDfOApkhW1LOs5SJAhYWwEMViHhoX2hjxImdWdtwA0m55/VanxfdSiXiNyRZcBAk+SqBaG8T/HbCLfcWVW/P/XUU9dCpHY3Q8e77Xfraa8X66o42HqbT3+KBpaUWDVgKTi/tRNwBYiMnlEgK0LFYfWnqulZXTOBIjklwzfprBG51YuwRBtaNbS8vdugeQ02KYtPK5ECrNb0VM0rq0quIr4TMNcKeuPHqUy/W/0FujaemMu6KaDW1Yn/lF1wc6FV/e63NFKizhj7KGIdBbJHf2jtSqoBqX03bV916qldLRg9hUbo+X2CbYKpXRt63hzWjXJo4mDDvEqbrq3FqesUGoO8TO8sAJzhM/S8OSrMrHnhsiV7AetTBqsrKK03VkPNGqnboKtRICsqJqrfTMNdmsfRMKhTnj6WpyTKrMFTTTss4DYFZreDdbN+UqSXPceKf9SG/9SppqWUEhr9rmaJVHQ29Lx5wWYDvuot50Rn77SmayjYa5gZeeQ3NjLA3ge/+USGwzDRByFQ+sEqVARsHOVaWoYUNCf4CLqE3XL63SEq488Rspk6eTaGlhAhpK2va2up8kdnQd1qQcoYWnZwnoFTN7furaU1FEewfiXKVES8lpN8CTKM11L2G3lbMfMokBODZZdg5hZYnkaaXbwmFwFBUTqx30fYeazpGwxdnwwByboJVKzFUBA8/c9//nPUVO2bpBkX8GUl840TfmWPNX996HqzWGLKgiCmnJtxXrugNdE9wwF9G3GNN2u2eIJMvGGiJaRGIky5/2W7IBJhsrbANIvJ7/N7azNe0aNsIj79qVJ+suuqDqglTZR7b9OucXgTg6Y242ULfnCk7tD2yXRfNqT0vJR4e6pU/bWdmMQ8au7ZJNNmiZzg9w4jLdMkm8OBUBXnVfHt/yucSXxd5NhppElnjrsFgoe+j1+/FIX4R6m6tZFYozBmafLdN0fb70V3lrTrzdQe+j6e7jreNl7YvncSBQkqK2qZ/lC1fP3Pavlf/645gZwYmGl5+lhOIGw2wxp/N6uDxey3it80Hc9ppPTEqQt5NAQhCQyWfKe31tTjWrPZyhCsM2/jUSs2nQaLj48jEbb2NYuesZARQfFWQLjmbMG6I1AoTiFNsORFGQVyIkH8wgsv7BoksuhswNoaiCusuVV6NSu1+xYUJhBr9z71AY/OaqJAKF7rVE1AHVnRf62/3ahTv7uRAo1Q9vsLrE5G1uMTULLk7Rt8UNaV52v2lDSLpBo16ft41+OR3a3DTCkPG68qbq6s2jmXJrt2f25VqpiLYCkgXIxk6Py/YMmVvluriIKU1vJURYO3Cf5TEiV8+M2ulZW1puD/moyGhkTZ52U0NWGy2R9rv4fqQtxHfMCDQoYdq9HnKJBF7UdjJaXJee4RcSos2oIwLE6TVZq1QhmyTobWDxsNBaArFiR4rRl/8qlaltw2C69YFwVY6ikewLsz5vjD29XU80q8qLnmpe1vYUxwp80UX126XXdvR+TdYSiugHzdFp8QRoClUtmKQOwEVRyk7KKKoobe7yOhYW3eeuuta9dfqdpvvvnmJu6jpp/g9zf8ioCpUn3o/XDlef3DrFddlsUS1hz7+DCDkeKw5xs4Vu8u10aBHDGzIUuuudMsN2ChbGlIEwVI8GGgXDPuJzfHNNt7OJDanA0bD72XtRVbuQ8Fj7W7EEh1DwSL+3AKmTHHD6fp289oXREm2nMBUSZbihnh0YpJmw2TxwGO5HYfpnLasPD1w7HhxD0Knm/pXjBUMZwsFPeGwQiYSe18P2ZU3yjPixXV/+iEzenu7NIgAKtlaAia/7OsGUJD9/9vltnp3NpQJFK37XtrtuakiQ9LBMgIQl9xD7Rv6NiRPA5znC0Do8KhAqtZJ1vMMCmdr8lqHt3XWlORT9GupsaETmvWhrvH5mPFH3Lm9aHcl2ge3JfCV/dVP7dLp3l1MymOCkWd2CvK3No94VmojVEp++RafdNGgRwQrA9WepW9UvpYcoixtdPHsloV42Rh2zTlvGOsS2/zTqCit3z6WvQ3c3rtqbtPcsmB4HDtTurjtcaiuFPEvDplduLMbVUW5tbuibfEfdjfjSHw+PTTT+9k2cGz8C45sFo2huyk2hqU2ue1Y08b3OcJhBKpEK5W0Mt6gUuuD2gwmHVqmh+h0gTCLZ46MxwIQRZpBXJOUu7X4yWn9KYgMhRrfV/H7S2vDd5t/AC5VVHkMjPvYN9/yUdbJ4wGCFmHRsGWGrflTrY2h/tjhbgvxZFcWoojmyNxqe0rCNR6nFEgNpx1omi3ajQsY2Dcse4PH9fmppqBrcV29oXim9amLKWq9imTU8772IfBWPZYSvGNN964PpkctAuFL7hEl0bT3QhSi2/Ba1uCwbbejI5FxS1DKGIq99OwnDq2XqIg4bKwJtw8zcG2AV0rg2XryQHVt6A/vi6j8FJb2jASybgmTDY/w544xXz7QyaFoK+koPr3obl7PFhMr7YHl+gPLbBc/ygKhCXKUj8HpYpxSuNs2FDV9c8999zFuTQoDPfvvlmc6E+olMJ7DoLE/UgCYCi4T3GeXLKUp9cuMVEGje2DEiWsk/2O5ufQuTgF2Smzccfx+cHier6UcFl7y4Z9nz4IDILUQtfR1KN1OJf4QEIE8yxz3rmx2kyXpkAKNBMcufOa8Kb9/Tncp3GnuawqKHRvah1Krrg0b0MnjvaBdZFAw8Daaszrw1xZUrYZSWht/+eiP1grIwKmgUmXdKTFNCGfuAVvxvA59XpqtrtHQtQR10krhXkpdC+gXPvzgozSOAmXczEcKMjnn39+d2943X3VnoXL+pIaa+auJVwpEcqTcMUL9Yo7p+zCpdFoj/Mucdl57SAFpWUhXFLPnCxRzORY38KXtXRu9Q6l+JWmauM42lZ4dCmCpIy0iq+aJV4jynO632VFcu5aiiT31qUUlEqSoDjyMLQmdbI9p/1eM9gMYTRf1gcdhOaVv1/K0RYj2VBZn/kKm/J1bhZ5vZDcc9PrWGONb91qzcNd6M5lKSZgk1Gs+B3tO6Gd0/02RZOhIE19OfIYLiFxhlyrpb2kCXvbfdsP9vs5Tm0sYaKkiYpinULwwd5lPGbCaL7oElp+12iwSYNcGYRqueDnuAZoTGA2tc6JhBC1qcraOXdBQohQIJ02KyYrnfdcq+3de9l3KtM7iV1C/KtsNHxeryjGU6ewc0xpzkBsKJ7ntau37/duMPrQ+ufk3jhXhqpdCYaysB1l+UTLyjjXvk9oDDYS36jHjrjceOcsSNC7+E9NJavaLTPtHO+btc1oaExzVel4nQFx7oaDe+6UWYwLL0ieqIPtOWeYkmtVo1c8iwf2aiT7cBkaBIrFPucAG6uzmQ+NLsVMVWefc4pjKYssz4oMbSpKtK6e59y6pDhQxaO5Ls957Gtu6dqblN5JgOTOOueC0dzV7ld2En53DS+ccwE1/k6+4fEMZfR2jVLZ2/fVfbQTyFb7P93kaNec6zIUmn3NCj1nAZpVUiZKLkuCxenjXHLhH1UD4J5LcWSNv/jii7tN5t7PfW44l+0zzzxz3RerbBx7vpqIcz11N6aBwnC/1UOQBedckW8vy76iPCgS845y38Je6/5quuYLaOrcWed4nOW+sqgUhvtMiBAslxD/QetcOZhMUI0wcX3rFdiPo3vN8jyandHQsEtw4xTzYDB0ysbrzcM4R4OR/FpmXLlPypKcswZbbltym1O3NSizFBqSVy3M3tJ4MVHBREe8c/SJ57bCPGUh5QetCd0lZCK1DmhcWxP/J1jP7RTm3vC2++LCYYl6rP5l632vbmONN64V3xOwxQSszbkpkebZN1iL8rDHGQ2lcV9CAoH7d9og5zKSJVaUxr+X78JUhEml8BY8zX0ui5ngYHVxaTS9rcH0l1IDQ3lAzdZqKld84NwEqnvLHyygSFA2AhQfXEoLHzHOAskljyRQKRT1QOcW+yn24R5rMOnxiKNeV7EOpSvjd7zPcKJArMNehoyxwAssE7KYy7VzOoUQIoJqLBGLWIdKzGVBL2laWzMRaiTp/l3DUDVhO5fTVsOiahgq7lUA9dxjHx+Mg0BDtKxNyuPcakLqruu+CE4nEYoE3d33ufD3Teluf+euR3trYp97bS90L1uBwiiI7oMx2zksYim67pOCLP5Re+dz94F/mGAlPFikgmuYCe2tU+N8zyVpoh5X+NopkzWGtz1eivtq6caKxgQJOvecIXEujTUzkEA7FwqluT9bnvVyF+j1hbbLmTf2hRNnrY3u/T02ky+qwaBFVq17Lu1NynnvBJL/mxI5N1fdbVw7BIh7x1wJ2IbPnEMtUGNKUyQFT2umeEmte0DCBP4v81BRIYGSW5OLY+v3WMsShmLxDkkDjXfda/rqhtzW5F0uLPu9uK/1uXfc02YqX7zq3HrJn0NhHUuzDDMoja/g4SVOaVu6MaxB1pnnNtzWrTT0tTnqedZMcIbRuRfLPsmYQvNcPNbiH//4x06pHHzw0BFgnwsWV+vUrPtm4Xjt0miO1jwNDIgMBWvTBMN773WLzjJ7++23dwtevnAbbctVutxV7qW5120aAtRx/lKnMVqPKrGtBcHCMhFoZL1t3b1j0+DdMu1sFjwOZSJdIt3dN4HKULAmGY/WxTrJxNvqvTlduKcGxbmvUlkbHHaJNM+tV9fxOhLY8/Y7ut8rmaQMlVL9Xn755euCO9boloNOtSchNOq+W0uPJnVdKlMV/7EuGIwQsSastdZqq+mLtWvPIs0th6fPJbZ3F4h12Q+MBetEyFqfhO6WXTzkFDozCksKKW39rbfeurrk0d32NDAQ7AGGdaczyuOedX8ProWsY2zWaQ24fDEFs0XXVSeQ2lcnRCgWTHWpDLV0ZaVAGA6Ea80Wt1oTYphSTeNyw9Y0kaA814r725xCCFMGQlapNN/cvFtta8+vL7PO/ek0UPPMGmle+l5vFg6FwcvAwILG4N5jjf6/Wpd1ks/YwpczXx+VLaawuS9CcXlvjm3NQLl0pqq9NQHbTHBrVe3E1tKb3UeFkssAcUHESxzn+igX5quvvnrdvqh52sXAtnY/NcnEy5ID0J8y7DRyiYkyH+ZxcAKxJrwNjGonTnS3RveQh+9H62uw54PLoYettTm3QTARWKgGBrlHGWeXlrr7uD5RNRa0Vrm1ys7YmuVG+FF6aMw108naBiFkzm1o1F2BvhkPNZe0bq3V1lxZdVRumBIDsQapl5h59SiQ49bKvqigusA63LHF//v+0XolUSIYydG2rKwtZWhYIJaIDUIgSlW2eBapBmvDUO8rW8f/GiuySOraWcXylk5UFUhSIKxsx3SnUPc59H5/f9T/zvqgvzXKct8SzWvV0bzz9jxervfT0Pz9E3ptXbj7KFseGXS37++YMPX+f6R72YRlL5Ty2ujXrQRRLQxrEyhBi1aQcJTH/1qjVadaJ/TOiqsgayvKoyycYncpwboODL0fttqtU/PSWaL2SNPrtmK5N4oaGIrFcdC9VNWh9/seh0Yb1KHaaT3D645tXh7OWuIfc+Ko/L8PzxW0BYvEQvH32QgVFhGEhMow1IevGXoTHixRSpeirYPxFsb81jyOsmiuTXU+51DjcAhhYp3qj0SR5M6yXltw86JxfIp/33333d1jLXk8Dq3/19Cy3+t9WDJFcZA7tDd5+IhTDyECmEDxwRhqC5PbCgg5ni37wFAk5b1fygzw2zJVhYV4oN5Ry0lua/79BAW6l6ZdGnr1P+c6cXAfiSbiRizPqvOtIR4o8WQLfa9yx+Waa2TxxLz+F4ws61Sqs1Nbg+bIAdmYd1YgHQkrvpIO11Sv5RS/tS5OLotccJgqd4YNckmN1G4DAVR0bvynU0iuAOunc++aq7crkKq2wW/1WBX6nD4eHVQteabsS2uI/taN4bX2BBCxLooud2sJQAzeofGj+6I14rb5TxRLyuSWcvLhCy+88MJDLc8JEYLFFxDEd4zUHxw2gN+G8f3+gucah4FFm0Dqk1sesEjfeeedHXNp/VH32rVa8eiO3ny56O13+s21ZJmY1+NTO+1z3gYCpV5hguqd4tbqyqperbkulCDat/8vuXDwJqcQ61UNCNovZf0t1+7DG3AhCtdAFkkt3wu2rW1R3LwNgbH83nLbbZDmfgzzPBqELkGiEK+6IJvTWrJErd8aq9MbXcq4ofhyu/rtDZIa+j4+M8eaESiy1qqlsWdYomts9S5GVwpqWYPgNFIHgq12UjimvCyQ7tRW00lGGFl/i7qQDz8e5k8EMQUfXFB1bUzV78U0mMuGwERNn/P/qf24mU+5gUuEMiZjjXbcXZsryO9BV/SvqjYfvv/vpdvomYObmrHYSaSODfZ5PbLWtneqXaLwitvkhkHvSxhPfV/wLtRUs0xVct56WsP//ve/d1cgPqTAZD30y2gqaLWmI2Knj+aa+L2lI9Oyl9a6+z71ARUUYSo0zpDIcFjTSY5hQJi88sorO5p7Thh2ehqj4WaoEWF9xJzmrF+nuTVVcvt9uaszGPAk5aGFv+dD9yeDXOwE0uRC+4ZCIfvJzhumcj+68RrmIZz5lz1n0UfAtfiWWRuloZVZwIpu3seW+zqdgqkI4ZIRGofZ1DpYSz0F+kZblrPnuSsbIjVVyDd3A6I7JWI9tf4uU6eW32sZ/0seUR7kUEkejWh1D5cypnhfcc86ctvz1dNYy1scEh6t6Wt/TogUWCOcfdmzzz67Csveb2CBYnQNEqtELfe/lgbDMDdDVcksUAK5U5yNmdV36vXEk9V74FNZZAV8E4SXOvPjPnU01kzCScXD1U6V1bYGYzFXddminZAZvAWBh543AwOR8VXbqmXXcsqDsXiDxKNHv+hDGgOKkXxgmS5prVMyFoHmuFXNgiN3mSUx1mRj3F6BEM7WlQXfbBjGhJkxKZVTZ17VHLPxvH5rp2QBQXw69LydUrZ3rGUxEW6hMnUEqE9dUMowyCNC9vid7X3xkJJnhp63c1tbN4aXPYXu9k/ZjbU9uZMC8QH1SSlQXaps1el85afMY6fYsp4IDQvRdEXCcCstWNaW3mndqlDHYM1QTmGfKp3bbyNACvR2DMenzbYYl+XdFIhTOyFi79jjtQOilIuHnZIna5aI/6ScGzPh/wRedV9Dy9vBidP+sc+bHV9mm/VkmD1BKT/ewi+XvrYHdW5sVCztdIqsrBr9+V0EW2N4C/bGcMMkd8uxR9+sz5InXG+uwKnqQljFzz333M5tieErbvV70Z5RMYWDd+/kYK+jMYVhf7WvyIKynE7huqo9DQHX1NTicn4v+k+q/t3SuKvet5+ef/75633ViY9h/ph44pNTO2umWCovohHONFNdMI8d7PX9rOE0J2ukzqsYqerzYZK7B6mtZ2mSFWoRJMWVjj3dL8ZeznHPwMGL6B6PDg1vD8ZgAepcWhQyC5/yyMVx7PgSelf3UbFgCqXHSZi4Xzp8tTPV1nSyr2fWYwyHJx8da6iHkBRJbi0uooh4TP9ojb/ceKckwgWjc2N4nVUyAbX7nUIIY4I6a88aS+emOCgRXQuO1ZySIMvidPpoMI7fgdZZqXh06He/wCqaZpWif0kLHiXPHPP02VyaMu1q/tfc+8YXT+ru/VoZWb8SJ5w60bpppVyYyf9bK5CClnW5xTwEtQ1dFkzHymMQseZ+y0pzv4VQ8VsajPSvf/1rmOOeSpol3zCxure63jWb+FgZWXgswdao2hpk+k3oTpFM/OP+pxA0tccZDvZ9Y6CzSv1f0PoYdT5oXgcMNO60mfKoG+/Q7n6ogt86MxAVGnbC97xTyj//+c/bKxBE8gHAAi27iduo3HHPDx1zqLtqgqw03VLOLEKujTl93B9Oc50+miUAjcRs5vShA+osoPyxjSlVq4DvSj0cH/j+gtXWmPC23hkReR6sdzNkDp3xVJuVPAp1XK7YzW8jd6byfD/ZWA3iQn/rW2806+z1jLRbK5AYq2FDPsQXYKJ84QXYD3m8dWN1WC3FuFYWzW7HdJPKt7902Sz9eqFhphS39WepNgr3UG0rajdfxwE8xlImUGSRxHdz+tgPSkhAU26L0qQpbcYCnqhi/VAn0JJ2MlZKzyd7yr4r7XhStvd34pPdZn+XKo8G9RJspMcHjPObB1WLh9ioTiKOt9VbYC4+SgLGhj5EDnhTx9wgv12TtWpnAGWNDEPsB/k+rXUjUAmRWmijdy6GfZ9ESiut82q9r9CZ4GB94oncGUOv/WU4JsDRuHhjg4jQJYV9iHVvUFQt2wuiN7O9GFjG49Bsf4ZDTXQzEhsFLgYl5u01fLBImb5dtSpCFszESLUO6XhZgGuflknz2lmgrB7MU256VedVpvodwwz73cxOGeiu1xDB0cYN1jxlsk93Qo0dO/UQJI2qRXPf7RrGHkGyf8MB8osTJASHPV6avEcnwX26Dmt8qskjXkLfxlTnsuz0Me36DxMmKP5ZTZ3DAlpXb2PNGyH94LaCnJXpwxUQtqkRl2BZKhObeh8TwfK5YqY6gxIijtTdRMzElTZjLA+TuICmubGq+q7dd8kMnQQI930ormIeeMpvqIVFflnfVbEj5h5a7bfLgzW3vzvlZUzYd9afq5iLyV6HfSRulFWJpr6jmpQC5rkyK2oeWu0XThqNN27PL2fFWHsto/DBe0Xat9NOtTHhSiK8uZIQuBGYvtj7XMeA9xEm5SHXNA8z+X9MTZjU8wozTyO1w4CiQPOEej2xVIQ7Cdb2grL3Gmv1Ce0PHgs8BuIbNcj02fhsGdTnyvAb9qGwBh+uxO2rXIcec2/Za4zI4mOlft71uwglPMY4KdOLTPHYfPPlyO1xXx0ucSG3dONvm/povau/SxY/uO0XpDRoIJuX5VFTM4IkhqsYxbW7WAqOTYRVXWExZz5Z//eZHguo1ZJ6mOBwAfWK+EqlLFe8WRI1ukOLjr+3LWjyd8W4fG4nzUbWMhrwHfrjO++bU+dhQEkUqG7WfLTPrchYSKATOtLnb1uLU381n1VXbcKr00ZZdoDutSkaGh22VAI6/aEB2VscrLq7B3f5cAxj4+YyKsWLRUioe50CqNmiv+kEcZOCMT+MdeMzMSmBwRptCHx++PrgNK9iiH9Y32guBJahTS9xwv+zUDstOJWgX5XLNzkhVPncbHY+cN8ZE+MBz/FHQVxCZk6dh68CJ6zR06N1b9hY1iqFIqHB8zpT3CQukgs0WYFv+nyypIattdXotFPvu6HP4WquMgzRtvTt5q7U9fq9RrV375VUtTehoYdK/ZIwRC22S7ssMFOmFLBaa96VoOi0UX/6jk7SywgRzykLLhJ/W5n9sSqiLxkVbVr7phZmlVLktYFPCVAsZes05Ms17+/vY8T6WBFY+CeXKEFSoWDDbyBLdU3Djs61PgCNqvmqjRA6MiB6nouTTCi9vgFFdfVtAl6pt+hXDKvZ5qXq9525zHqtOSVDm8Oi2DI6oB/a2ncKSNF2US5xt6Nt+cBcDJgGfNGy2KchL4RAM5ZdA8IhxVK7BH7QevHkb/NDKSUCyv+rOWEN+R35X4fox2Gq4iBOnimKUmzRB00rQsJoDAn0J0xcwxdoXP0Ihiz1OmPEe7KC0DdeSfjEWwyQoctx0jsbZ11Vel4ASj+3VrHIemktA69eQ/MaX/773/++TrwoxlkjzKbl5dIsvkLWoPucPo7TWDM3cWnTGfTLzLwHd/0CVmiBTVZBqbbLrKlS76pcrzEfBqijZm4KTJQ/tSKmqsxZpAmkNGNFjRhrKlGP2+Kkk1+ZUXUjQEP0Itgr7vSYICBAPG9IFdr7P0uzFvElQyxndDch0Wt1ic2tMTQ5PKw5gy1lziioR1p8wIBsDkvKv0FF7fGUi/2dcKpRY3HT3FPJhk6aXve+adl+3CaLeXcyFJpa2sylOysQTFAP/mo/Sr1LsNdrviZ3GMtjqYABsxA+ZX1QRh1j3YAfzlJtjGmWapk/Q+zjoeC5R7RDA8LDyRBtGBYJeo9oWrZO7gfXSo5IwZSijTd8Xu/XsJGyqIAVTzT3ZehxXIs05V92XPs+AyKD0nWnhQpBMwwbnUs++JtOKKXjd7J0gvU+SuaDAfbpMnE81POujCvPM/oad/zgPl+gMyelwDphNQiCFamv8K9e8pglF1dtCVIY5fMXWyl43hHX57gJn0kY5YOnaEaQnCZXvKy4elF5LFMqV0TCIXeH94hlUTbNaymu0d8VD0PbjtG5Mf3/6aef3v0frw0tjhtMzyfueSeK9nO+8uKc9mhz6hkFS+VSb6tlpXsCCb3xRyePDI7oP7Q4fkC94u16oBULvVMa7wetEsLk5Zdf3g358f+aKlbcl+VSG4zcT7XfLue/QkQ5xxWJ5fssHxxDxqQF2ofIp0npTRAQAGhCYNSfCrMtM2aKW+TOrMgUonuTD3N/VWtSnCxf+RSQraPFCTdWrUbQrUw8dMIPCRqKvvgn44/gKXOvhqz+rqaNxT/reed9+KTTzNDh+CfPaIkGDZST0HTnIPoH/aMUg8es0YJpTh65HRIi4hkEQr1VEg6Y0t9K30z5UCb9UCcZ749JK3YZIp8uIyuXRlMgCQDCndCv2K/MLO+rwrgTqOtZmOWeZ2j4DDxSJTIeyCCZ9T8NcjlR+DwGuTCb2ZGro+l2TipeF4TNA9G43LwRaIpnvLd2GfZ88Raf5bVJ0z9tcWEp23kEcmM+2McXYIiaG1Yh3jG3DBoMQxDkgmiOAwHTqQIzZc16T7nfZXE0eQwzjevqtMgtgW4vvfTS9QCq+qBR9vigwkAKwN+gMdcn46BU8GVaYKcan4He8U5jlV977bVZ/xNn4jEU6j3HIOzUUXak67W/wR+wzOThxlwaCXW2IEPMnsAnvqdZ9z5rMq9OCzQld9EJGBB64+1FgdBKPrxMjWpAXMtVRSjUiDF/aL40SsRR1jUCAuM1mjQfOSYrK6AA/hD29JZJ6dZoVWfk4liEShPu6qvz1FNP7eoHUjQYEb+UUOH/tY3u1MoNEv1n3U+Lij2dEpaTAZeJE7XdR6+8BrUlKkheVmYubTyAdxpOV10R+TBV5+uge3Grisb3dgIp2BIzvP3229f+soQ/hqsqvZQwf1O7YH9b1kXpwPnQMWWKZ5THuqySGuBhrPL5XSteVREgmhX76DTSEJtcWgROfFABaamD/nZSONfV1sb+fuONN66tUwK/gUQZFBkOFX1CKbvV9nidzPB5JdUwHvDG0Hw9sKfbt2VK7k2BsCCaCresGPWF5YwvJ4thtvyhGChLpnqAum9isloo+MGOuEPM9aCMGa6MpWuj/HF8UZ5/6Zn4o3n2xcwaHJT/vA6/uTLe6/w5WJESabxxKdnoFWp3UfuLFAU6+rvois72N57IaGimUK7wWe/1nELs9/pgvdeiaL+FJ5jFBxMWWY/iFrmfMFpBc9ZLsQ9KBnOlWGrZXCO9enBN76N1trsgFNAK3RpC5XpjUQmEqs/rn1Y9CD946b6NySVwGBu5N2ad14UqySsgrjC0DMmKfsvSW84QWhaSdlot2aKJg1U8z1qvr5A4+qLjg31/Qd1xCYlaX5SF4zqGa2BMHTdrE10ls/9XXERhVLU6mRjrdWWxJNGn1O5StRkDnTrL9We11r1ZamcMWVWya6Vw1lRv1nmdMbBqfOoeQejbr1zWuZztce/FG+RCPfM6sVYvUvPEUv9njdfrdahAdO8KpN5UmKg55QQMIdIJpdYEMVcWK6FRuidGIlAoEMw3LbvXb5mUfVM3AnxQDUCz1UPFhvVYcq1WFhi0Cuf3ipUGK0V1PtGXAViRaWm43sPzIPXTPkfzptqRB2SFfY7myQDvnfVdrwIht+/VyuRJxSdVlZffXyuKZkqUzdG16knK2iqQXtHK9D1aP4pl1cadUCj3v+SHaI7ejAopnQ0MqueV94l1zcljO6m9nSJyP9nD77zzzq4FUZ2Wo7/3ojkZkSyoUWZx0VnXbSRSPDjUF6hML/WyhoeERvERwqLagbKtskT5wP19wmgfo3EHxwFrkiDgepKuy4AQw6jlN2MAzfGA1O0GT8UPFRZOe/5tVaijbd2ym9Hjell5FAmjAK25se3p4iR13oWh+3bwnlfosJYJphBE73ja7Ie6dLI86uhaBoa/cYTt9DLE2hYIiGjP8qzXVQHTXFyUTa0R8ntX1T5ZV9sCI6H6LjEwNMz9XJp384GiM95Yvh+PzFpuDof9AkyjEKzAC2FRmmepu66rTF7Ol6g6eQi0TTQvphROab4F1rM6y9gqgSLFMo0StwmnyXpWZTDok7esAckorN2//zcsatZwFMgj+2VhLAzTMKJ6ZDmdcHeUicUiZbFMFsZ5KJEKyZow2HhjFmf9z4DSqCfSrN22feOdKiiRDAV7XuuLGi/iAYYkuTAt2keB3KgIhbKo+2btLjBXwdaycDDWEOY8EF0JlYQJxcEnjheWcyVmvc4HlEQFZ8uR1tWJKB7MaJz1GgVyq3TPWnmzRqo41QNrUnXPW6CUvu2UWbfVLNJZo/OMi3Bh1boGGnE7c81HgQwGg8FgFMhgMBgMBqNABoPBYDAKZDAYDAajQAaDwWAwCmQwGAwGg1Egg8FgMBgFMhgMBoNRIIPBYDAYBTIYDAaDUSCDwWAwGIwCGQwGg8EokMFgMBiMAhkMBoPBKJDBYDAYjAIZDAaDwWAUyGAwGAxGgQwGg8FgFMhgMBgMRoEMBoPBYBTIYDAYDAajQAaDwWAwCmQwGAwGo0AGg8FgMApkMBgMBqNABoPBYDAYBTIYDAaDUSCDwWAwGAUyGAwGg1Egg8FgMBgFMhgMBoPBKJDBYDAYjAIZDAaDwSiQwWAwGIwCGQwGg8EokMFgMBgMRoEMBoPBYBTIYDAYDEaBDAaDwWAUyGAwGAwuG/8HZn1QtRuXR2kAAAAASUVORK5CYII="
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import net.imglib2.type.numeric.real.FloatType\n",
    "\n",
    "tubeImg = ij.op().run(\"create.img\", [400, 200], new FloatType())\n",
    "randomAccess = tubeImg.randomAccess()\n",
    "randomAccess.setPosition(0, 0)\n",
    "randomAccess.setPosition(100, 1)\n",
    "while(randomAccess.getLongPosition(0) < tubeImg.dimension(0)){\n",
    "    x = randomAccess.getDoublePosition(0)\n",
    "    y = (50 * Math.sin(x / 20)) as int\n",
    "    for(offset in -10..10){\n",
    "        randomAccess.setPosition(100 + y - offset, 1)\n",
    "        randomAccess.get().set(120)\n",
    "    }\n",
    "    randomAccess.fwd(0)\n",
    "}\n",
    "\n",
    "input = ij.op().run(\"create.img\", tubeImg)\n",
    "\n",
    "ij.op().run(\"addPoissonNoise\", input, tubeImg)\n",
    "\n",
    "ij.notebook().display(input)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "java.util.concurrent.ExecutionException: java.lang.NoClassDefFoundError: net/imglib2/loops/LoopBuilder$TriConsumer\n",
      "\tat java.util.concurrent.FutureTask.report(FutureTask.java:122)\n",
      "\tat java.util.concurrent.FutureTask.get(FutureTask.java:192)\n",
      "\tat net.imglib2.algorithm.gradient.PartialDerivative.gradientCentralDifferenceParallel(PartialDerivative.java:163)\n",
      "\tat net.imglib2.algorithm.gradient.HessianMatrix.calculateMatrix(HessianMatrix.java:284)\n",
      "\tat net.imglib2.algorithm.gradient.HessianMatrix.calculateMatrix(HessianMatrix.java:237)\n",
      "\tat net.imagej.ops.filter.tubeness.DefaultTubeness.compute(DefaultTubeness.java:175)\n",
      "\tat net.imagej.ops.filter.tubeness.DefaultTubeness.compute(DefaultTubeness.java:97)\n",
      "\tat net.imagej.ops.special.hybrid.UnaryHybridCF.run(UnaryHybridCF.java:75)\n",
      "\tat net.imagej.ops.special.hybrid.UnaryHybridCF.run(UnaryHybridCF.java:97)\n",
      "\tat org.scijava.command.CommandModule.run(CommandModule.java:199)\n",
      "\tat net.imagej.ops.OpEnvironment.run(OpEnvironment.java:944)\n",
      "\tat net.imagej.ops.OpEnvironment.run(OpEnvironment.java:135)\n",
      "\tat sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)\n",
      "\tat sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)\n",
      "\tat sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)\n",
      "\tat java.lang.reflect.Method.invoke(Method.java:498)\n",
      "\tat org.codehaus.groovy.runtime.callsite.PojoMetaMethodSite$PojoCachedMethodSite.invoke(PojoMetaMethodSite.java:189)\n",
      "\tat org.codehaus.groovy.runtime.callsite.PojoMetaMethodSite.call(PojoMetaMethodSite.java:53)\n",
      "\tat org.codehaus.groovy.runtime.callsite.CallSiteArray.defaultCall(CallSiteArray.java:45)\n",
      "\tat org.codehaus.groovy.runtime.callsite.AbstractCallSite.call(AbstractCallSite.java:110)\n",
      "\tat script1543854201325.run(script1543854201325:5)\n",
      "\tat com.twosigma.beakerx.groovy.evaluator.GroovyCodeRunner.runScript(GroovyCodeRunner.java:94)\n",
      "\tat com.twosigma.beakerx.groovy.evaluator.GroovyCodeRunner.call(GroovyCodeRunner.java:59)\n",
      "\tat com.twosigma.beakerx.groovy.evaluator.GroovyCodeRunner.call(GroovyCodeRunner.java:32)\n",
      "\tat java.util.concurrent.FutureTask.run(FutureTask.java:266)\n",
      "\tat java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1142)\n",
      "\tat java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:617)\n",
      "\tat java.lang.Thread.run(Thread.java:745)\n",
      "Caused by: java.lang.NoClassDefFoundError: net/imglib2/loops/LoopBuilder$TriConsumer\n",
      "\tat java.lang.Class.getDeclaredConstructors0(Native Method)\n",
      "\tat java.lang.Class.privateGetDeclaredConstructors(Class.java:2671)\n",
      "\tat java.lang.Class.getConstructor0(Class.java:3075)\n",
      "\tat java.lang.Class.getConstructor(Class.java:1825)\n",
      "\tat net.imglib2.loops.ClassCopyProvider.newInstanceForKey(ClassCopyProvider.java:103)\n",
      "\tat net.imglib2.loops.LoopBuilder$RunnableFactory.bindActionToSamplers(LoopBuilder.java:186)\n",
      "\tat net.imglib2.loops.LoopBuilder.forEachPixel(LoopBuilder.java:120)\n",
      "\tat net.imglib2.algorithm.gradient.PartialDerivative.gradientCentralDifference(PartialDerivative.java:187)\n",
      "\tat net.imglib2.algorithm.gradient.PartialDerivative.lambda$gradientCentralDifferenceParallel$0(PartialDerivative.java:155)\n",
      "\t... 4 more\n",
      "Caused by: java.lang.ClassNotFoundException: net.imglib2.loops.LoopBuilder$TriConsumer\n",
      "\tat java.lang.ClassLoader.findClass(ClassLoader.java:530)\n",
      "\tat java.lang.ClassLoader.loadClass(ClassLoader.java:424)\n",
      "\tat java.lang.ClassLoader.loadClass(ClassLoader.java:357)\n",
      "\t... 13 more\n"
     ]
    },
    {
     "data": {
      "image/png": "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"
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sigma = 5 / Math.sqrt(2)\n",
    "calibration = [1, 1] as int[]\n",
    "output = ij.op().run(\"create.img\", input)\n",
    "\n",
    "ij.op().run(\"tubeness\", output, input, sigma, calibration)\n",
    "\n",
    "ij.notebook().display(output)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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