08a_hypothesis_generation/interactive_parameter_exploration.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "8b0b3f46-0e20-4eb8-a82f-3813b83be9ff",
   "metadata": {},
   "source": [
    "# Unsupervised machine learning for object differentiation\n",
    "In this notebook we attempt to differentiate 8-shaped objects from round objects using unsupervised machine learning. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "aa9612e0-12c0-4ca0-b3e3-82e9d50a4cd7",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "from skimage.io import imread\n",
    "from skimage.measure import label\n",
    "from napari_skimage_regionprops import regionprops_table\n",
    "import stackview\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from umap import UMAP\n",
    "import seaborn\n",
    "import pyclesperanto_prototype as cle\n",
    "from skimage.data import human_mitosis"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "09f7814e-bc34-46b0-a8b7-50ad119e6a25",
   "metadata": {},
   "source": [
    "We load and visualize the example data first."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "20e517f9-7698-47c4-86ee-c8890e671054",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<tr>\n",
       "<td>\n",
       "<img 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VmjVL9t73KgEOo0NIR8qt1Ln1LH8zo32bxDR/kXQUQFEaJUS/ux8fy5eXKrF9MvDmBTYsiklxP34a18OXfgrN7QdePHa9AMIOgIgAACAYWgBI3ScqtzBO3buAPYrp3cE963m3bnym4OOCau+1btCN4VQ8UMYEQCBPOxcDxjGNqybl4GBfUwIeoXoHwYJejAJARAowJ9XXBeKTSFBU27lz/S1fygc4Q+m4U93AAAAwxAAAZRl5ZjLjqzRM2HdX19cCBqAmwiAAGxhZ2AzLfwBgNsIgAAAAIZhEwhCxclLwITpDiFOKXdXsImVP1q/wbZt58Dv+9RJ5n2PETwEQMAlqduxeXE5GDdvBZctxOULhSaGvhTCX/BkC3yDjSEQwo9oAQMAABiGCiAAx1HlC68Ns34gSbrvR1/weCbOK6T6N9j7qAbCLwiACAVu/wY/8HPYSwW1XIoJcNmO1f+5MAXCUoMf4Gd8qwEAAAwTsSzL8noSCJZkMqloNOr1NLJyshJo5+5ftzeCuLkJxE/cuBWcn6t+0uCVv1z6VvBKPUYhxw4CJyqAfmsFJxIJVVdXez0NuIgAiKIRAMtHAHSXU0HQz+HP7tDmFL+HQSfbv34KgQRA89ACBgAAMAybQBAq37lwNBAbQty6JqDplT+n+LnyJwWn+udnbPxA2PENBwrg1N0/CGjusDOw+Tn8bZj1g8CFvyDOGQgDKoAInVRYs6MS6MZt3/qGQDsrgoRLe/k5+MFeVP9gAr7lAAAAhqECiNAqdz2gG9U/J1D5y65vBa/YXcFU/5y3YdYPfL8jGAgTAiBCrdh2sNeh7z//Y2hZbWDCX2GyBbr+oZDQByDMaAEDAAAYhgogjOB1Za8Yg1Xx+lYIqfjZh4of2PwBkxAAgYAh9AEAysWfOwGyc+dOzZo1S/F4XJFIRJs3b8543bIstbW1KR6Pa+jQoZoyZYoOHTqUMaanp0dLlixRbW2thg0bptmzZ+vkyZMu/hYAAMBrBMAAOXfunMaPH6/169dnfX3NmjVau3at1q9fr7179yoWi+n222/XmTNn0mNaW1u1adMmbdy4Ubt27dLZs2c1c+ZMXbp0ya1fAwB8aeqky766Py/gpIhlWZbXk0DxIpGINm3apDlz5kj6oPoXj8fV2tqqRx55RNIH1b66ujo9/vjjeuCBB5RIJDRy5Eg9//zzmj9/viTp7bff1pgxY7RlyxbdcccdBX12MplUNBp15PcCUJow3E3DL5eBcWMtoN+CZiKRUHV1tdfTgIuoAIbEsWPH1NXVpZaWlvRzlZWVmjx5snbv3i1J2rdvny5cuJAxJh6Pq6mpKT0GAACEHwEwJLq6uiRJdXV1Gc/X1dWlX+vq6tKQIUM0fPjwnGOy6enpUTKZzHgAQFj5rToHOIEAGDKRSCTjZ8uyBjzX32Bj2tvbFY1G048xY8bYMlcAMA3rDOEXBMCQiMVikjSgktfd3Z2uCsZiMfX29ur06dM5x2SzYsUKJRKJ9OPEiRM2zx4AALiJ6wCGRGNjo2KxmDo6OvTpT39aktTb26sdO3bo8ccflyQ1NzeroqJCHR0dmjdvniSps7NTBw8e1Jo1a3Ieu7KyUpWVlc7/EjDG3iPPDjrmprELXJgJkF2qSmfHhhAqfvAjAmCAnD17Vr/97W/TPx87dky//vWvVVNTo2uvvVatra1avXq1xo4dq7Fjx2r16tW65pprdO+990qSotGo7r//fi1btkwjRoxQTU2Nli9frnHjxmn69Ole/VowQCGBb7D3EAjzu+9HXwjFTmC/mTrpMncIQShxGZgA2b59u6ZOnTrg+QULFuiZZ56RZVlatWqVvv3tb+v06dOaMGGCvvWtb6mpqSk99v3339df//Vf64UXXtD58+d122236cknnyxqXR+XgUGhSgl+hSAM5hfUIOiXy8DkU2gYDFrVj8vAmIcAiKIRAFEoAqA3CIDOIQAiLAiAKBoBEIVwKvz1RxjMLmghMAjhL8wIgOYhAKJoBEDk41bw648gmJ/fAyEB0FsEQPOwshUAAMAw7AIGEAp7jzxLFTCPYipsfq8WAigfARDop/vJ7w94btRD8zyYSfB41f6FvfqGRafDIK1fwBu0gAEAAAzDJhAULUybQLJV+wpBRTA7v1QA3WoF/yJ6Nuvzn018xJXPd4tTVUCqf/7BJhDz0AKGcUoNfbmOQRgMv1xBL9/4MIXAVFBjbSAQHrSAAQAADEMFEMawo/KX67hUAcOp2Mpf2Nl1v2Fav4D3CIAwglPhL4wOjP951ufH/eZzLs/EW4S/7MrdIUz4A/yBFjAAAIBhqAAi1Nyq/KU+h1ZwsFH1K06xLWGqf4B/EAABQMEPf/++Yl3O12a0L3Hscwl1QDDRAgYAADAMFUCEVlA3fvzXrmfyvv6ZWxc6+vmpzR59N4OYtgHEDqmKopPXA8xX9es/zskqYNh99o17bTnOLz76gi3HAezAnUBQtKDcCcSLAFjOGsDBgl82TofBUnh5N5BS7gDiRuvXzhBYaOjLhzA4OLtCXy5+C4PcCcQ8BEAUze8B0OvKX7EhsJTg15cfQ6DkTRAkABaPMDiQ0+EvxU8hkABoHtYAAgAAGIY1gAgVr6t/XkhVEP1WCbxp7AJXqoClVP2CyO7KX//jUgl032ffuNdXVUCYhQAIhMR/7XrGlyFQcq4dbEr4gzvcav3m+kzCINxECxgAAMAwVAARCia2foOkb6Wu3GqgiVU/p9q/8JdUNZBKINxAAAQ8VO4O4CDqH+D6BkITwx38wYv2by6sDYQbaAEDAAAYhgogECJ+3AgyGKp+wEC0g+E0AiAAwFh+av0CbqIFDAAAYBgCIAAAgGEIgECIBG39nwnsvA+wk7jUjD/RooZTCIAAAACGIQACAAAYhgAIeIiWbXh9NvER29q/M9qX2HKcwfz7inXpB4Bw4zIwCIVRD82T5P0t4VLzKEYqBJZ7VxDCJOyULQS6FUQBOI8KIAAAgGGoAAIBR+WvdL+InnXkuE7s/E1V37xsz/b/bCqCQHBRAUSolNKCtetzy/3sYoLcZ25dmH7AP+xc9xcErBUEgosACAAAYJiIZVmW15NAsCSTSUWjUa+nMSi3NoTYXXXMtRmEap89nGr7Su5d9NmPlbegtoODcKHlX3z0Bcc/I5FIqLq62vHPgX+wBhChNeqheZ7vCi4FQc8ZQVrvN5i+YcuPYRD2cSP8wUy0gAEAAAxDBRCh5vT1Ab3adILCONnulYJzn18A6I81gChaUNYA5lNuICT4+V8Y1voVw+tWcFDXAKb4bS2g261f1gCahxYwAACAYQiAQJGo/vmfadU/hAsbP+AGWsAoWhhawCmFtoIJfcHg9Jo/yd8BkDZwebxuA3sZ/GgBm4cKIAAAgGHYBQyjDXatQCp/+T3wd8Vvpvn2cs4p/ClVgfOiEkjbF26jBYyihakFHERT9n2joHHbm5c5NodSgl9fToRAN9q/kr9bwCletYKD3gLuy60Q6JfgRwvYPFQAgQAoNPT1f48TIbDc8BdUQQh+sI/T1UC/BD+YizWAAAAAhqECCPhYKZW/bO+3oxJoZ+UvdSzWA8LvSqnU5aoaUvWDnxAAAZ8qN/zZyam27wN/931CIEKHoIcgoAUMAABgGAIgYAA/VRPhvDDtxgXgDFrAgM/4Kay5seM3COsB2QEMIGyoAAIAABiGAAgAKBttZyBYaAEDQA5Bbv2mApnTdwUh+AHBRAUQAADAMARAwEf8tAEkaIJcrQsqqn9AcNECBpCVqff8TQlLoJzRvsTWNjChDwgHKoABsnPnTs2aNUvxeFyRSESbN2/OeH3hwoWKRCIZj5tvvjljTE9Pj5YsWaLa2loNGzZMs2fP1smTJ138LQAAgNeoAAbIuXPnNH78eN1333266667so658847tWHDhvTPQ4YMyXi9tbVVP/rRj7Rx40aNGDFCy5Yt08yZM7Vv3z5deeWVjs4fyMbO6//1rdr9InrWluOEQd+qXbHVQCp+QDgRAANkxowZmjFjRt4xlZWVisViWV9LJBJ6+umn9fzzz2v69OmSpH/5l3/RmDFj9LOf/Ux33HGH7XMGvPLZxEeKCoFhC3255AuDboe9j7RfnfO1syved3EmgHloAYfM9u3bNWrUKF1//fX60pe+pO7u7vRr+/bt04ULF9TS0pJ+Lh6Pq6mpSbt37/ZiugAAwANUAENkxowZ+sIXvqCGhgYdO3ZMf/M3f6Np06Zp3759qqysVFdXl4YMGaLhw4dnvK+urk5dXV05j9vT06Oenp70z8lk0rHfAc7Y3rys4LFh2vxhSlWvVF60d/NV/bKNoxIIOIMAGCLz589P/3dTU5NuvPFGNTQ06JVXXtHcuXNzvs+yLEUikZyvt7e3a9WqVbbOFdltb17GpWAQOoWGvlzvJQQC9iMAhlh9fb0aGhp05MgRSVIsFlNvb69Onz6dUQXs7u7WxIkTcx5nxYoVWrp0afrnZDKpMWPGODdxw6WqdQTBYKt57K8KGvf7VU84PBNvlRP+ADiHNYAhdurUKZ04cUL19fWSpObmZlVUVKijoyM9prOzUwcPHswbACsrK1VdXZ3xAAAAwUUFMEDOnj2r3/72t+mfjx07pl//+teqqalRTU2N2tradNddd6m+vl5vvPGGHn30UdXW1urzn/+8JCkajer+++/XsmXLNGLECNXU1Gj58uUaN25celcw/MOOdnAxa/9SUpdlCdNaQCcVWukb7P1hrwQC8BcCYID88pe/1NSpU9M/p9qyCxYs0FNPPaUDBw7oueee07vvvqv6+npNnTpVL774oqqqqtLveeKJJ3TVVVdp3rx5On/+vG677TY988wzXAPQp8ppB5cS/lCYckNfrmMSAgG4hQAYIFOmTJFlWTlf/8lPfjLoMa6++mqtW7dO69bZd2soAAAQLARAIGSCVPmz8y4gbnGi+tf/2GGoBLL5A/A3AiAQAH1DXa52cJCCXxA5GfwAwG3sAgYAADAMFUD4yic/+qdFjX/tjeccmol/UelzH9U/s/xD7Qr95TvtXk8DcBQBEJ4pNuzlOoaJITDogrj2D95w6y4g/1C7Iu/PKQRDhAUtYAAAAMNQAYQn7Kj+9T0WVUD7cAHoD9H6DbdcVb5C30M1EEFGAASKdGT8kpyvjf0N11cMC8JfuJUS/oAwoQUMAABgGCqAcJ2d7d/+x7S7FZyv2pdrfJCrgLR/P0D1r3ypzRt+uyC0nZU/dgsjyAiAcI0Twc9JxYa//u8LchB0Wiposhs4/M6ueL+kEOjE7l/avsCHCIBwhVvhr9wNIaWGvlzHClIIpPpH5c8v7A5/BD9gINYAAgAAGIYKICB7K3/ZjhukSiBgt74VvVztYLcu+AzgAwRAOCpo6/6c4ud2MK1fuImgB/gDLWAAAADDEAAROp/86J9SeSwQ1T8AMBMtYIROsbuAnVr/BxTr96ue8HoKg/rV5s0lv/eGOXNsm0eh2AEMZEcFEAAAwDBUAOEY2rCZ2BGMfPxc/Sun6tf/OF5UAQEMRAAEDOT12j/uABIcdoW/bMdzIwz+5TvttIGBLGgBAwAAGIYKIAD8n1Qb1q1bwpnQ9vWDv3ynXZL9G0JSxwWCiAAIGMbr9i/8Hfwk98IfawIB79ACBgAAMAwVQIRKsdcAhPuCsAHk96uesLUN7PeKX4oXbd/UZ1IJBNxFAITtgnb5l7G/WWfExaBp/borKKHPL9xoB9u5I5j1fwg6AiAAZNE3wBVSDQx64AvTpo98yt0QYmfw+8Xq3xQ1/rOPjrftswHWAAIAABiGCiBsl1qH52YrmLV/cFLQq3tB4eZ6wP6VvEIqgnZU/4qt+vV/L1VA2IUACAAGM6X1Oxgn1/SVE/pyHYsgiHLRAgYAADAMFUA45rU3nnOlDUz7NxiCcPkXwG52Vv8AOxEAEUhBDH1jf7PO089PBTAuBwM4z+ngx3pAlIsWMAAAgGGoAMJRfSt1drWDnaj+papzJlwQ2gu0f2ES2r4IAgIgXJMruBUaDN1o+zp1VxCv2799fXv5PNrAgAMIfggSWsAAAACGoQIIzwVxQwcKQ+sXpXDjvsB286L6xzUBUQ4CIOAgP7V++3JjRzDhDwD8ixYwAACAYagAAv3YtSPYr9U/p1H5gx3cvC8wYCICIJCDCQHO7h3BhD8ACAYCIGA4u9YDhjH8ffrCewWP3V9xjYMzAQB7sQYQAADAMFQAAUgaWMHrXxEMY4VPKq7KV8xxqAiagYs/I6gIgACyCmvgk+wLfYV+BmEQgN/QAgYAADAMFUAARnGj+pfvM6kGFiYol39J3YWDVjCChgAIwAheBL9sPn3hPULgIIIS/rzGLeBQDlrAAAAAhiEAAgAAGCZiWZbl9SQQLMlkUtFo1OtpAAXzS/u3Pz+1glO3XvNS0Fu/bq4DtLv9m0gkVF1dbesx4W9UAAEAAAzDJhAAgG6YM8fTKmDQq39uYeMH7EIABBBafm39IpycviQM4Q92ogUMAABgGDaBoGhsAkFQ+L0C6KdNIH252QoOe+u33GqgW1U/NoGYhwCIohEAERQEQHs4FQjdCH/73s5+T+vm+Pcd/+yUQkKg1+1dAqB5aAEDAAAYhgogikYFEEFBBdBcuSp//blZCfQzKoDmoQIIAAiVQsNfamwx44GwIAAGRHt7u2666SZVVVVp1KhRmjNnjg4fPpwxxrIstbW1KR6Pa+jQoZoyZYoOHTqUMaanp0dLlixRbW2thg0bptmzZ+vkyZNu/ioA4AjCHFA4AmBA7NixQ4sWLdKePXvU0dGhixcvqqWlRefOnUuPWbNmjdauXav169dr7969isViuv3223XmzJn0mNbWVm3atEkbN27Url27dPbsWc2cOVOXLl3y4tcCAAAeYA1gQP3ud7/TqFGjtGPHDk2aNEmWZSkej6u1tVWPPPKIpA+qfXV1dXr88cf1wAMPKJFIaOTIkXr++ec1f/58SdLbb7+tMWPGaMuWLbrjjjsK+myv1wD+v/p7cr52S+f3XJwJSlU/dkrW5zuPbLf1c1gDaA47Kn8mrwdkDaB5uBNIQCUSCUlSTU2NJOnYsWPq6upSS0tLekxlZaUmT56s3bt364EHHtC+fft04cKFjDHxeFxNTU3avXt3zgDY09Ojnp6e9M/JZNKJXymvfKEv1zjCoL/kCn3ZxtgdBAEAmWgBB5BlWVq6dKluvfVWNTU1SZK6urokSXV1dRlj6+rq0q91dXVpyJAhGj58eM4x2bS3tysajaYfY8aMsfPXAQAALqMCGECLFy/Wq6++ql27dg14LRKJZPxsWdaA5/obbMyKFSu0dOnS9M/JZNK1EFho5S/Xe6kCeqeQit9g7y23Ephqsfq9FYzSsekDKA0BMGCWLFmil19+WTt37tTo0aPTz8diMUkfVPnq6+vTz3d3d6ergrFYTL29vTp9+nRGFbC7u1sTJ07M+ZmVlZWqrKy0+1dBCJUT+vIdz44g6KcQyNo/AF6jBRwQlmVp8eLFeumll7R161Y1NjZmvN7Y2KhYLKaOjo70c729vdqxY0c63DU3N6uioiJjTGdnpw4ePJg3AAIAgHChAhgQixYt0gsvvKAf/vCHqqqqSq/Zi0ajGjp0qCKRiFpbW7V69WqNHTtWY8eO1erVq3XNNdfo3nvvTY+9//77tWzZMo0YMUI1NTVavny5xo0bp+nTp3v562VVTvu3/zGC1Are+IfD875+91unXZpJceyu/uU6NhtEAKB8BMCAeOqppyRJU6ZMyXh+w4YNWrhwoSTp4Ycf1vnz5/XQQw/p9OnTmjBhgn7605+qqqoqPf6JJ57QVVddpXnz5un8+fO67bbb9Mwzz+jKK69061dBH4OFvVzv8WsIdEP92CklhUC/rAek/etPJl8CBmYiAAZEIZdrjEQiamtrU1tbW84xV199tdatW6d169bZODsAABAkBEAYwW87gkup/OV6v9fVQCdbv4N9Ju1gACgNm0AAl5Ub/rIdz+5jBkUp4ZMWLAAQAAEAAIxDCxjG8HpHsNNVOi82h3jR/s01B7+3g6k8AvATKoCAC9xq0ZraCpaKC6P7K65xNZAR/pzTHP9+WTt4y30/EFQEQBjHjusLwp/qx04pKQg6GdAIf/5F8IPJCIAAAACGYQ0gjOTWekAvWrKpz/T68jBBk69SV+jFo6n2eac5/n3te3teUeMBk0WsQq4wDPSRTCYVjUZd+Syn27VhDIB9OR0C/bAJJBe/bwqBs7KFQUJfbolEQtXV1V5PAy6iBQwAAGAYWsAAgNCh2gfkRwUQCDGT7xICAMiNAAgAAGAYWsAwkld3AwmbziPbfbsRpH7sFDaCwPcOT71uwHMf23bUg5nANARAGMnr28IBMFO2wFfoGIIh7EQLGAAAwDBUAAGUJdVm9WsrGPBaIVW/Yo9DNRDlIgDCaP0vNE1LOFxSoZS1gIO7b2nh/3ewYe1FB2cSLnaFv2zHJQSiHLSAAQAADEMFEIAt/LwjGNkVU/Ur5n1UCJ2r/GX7DCqBKAUBEIBtCIHBUGrwc/r4QQ+OboQ+wC4Ry7IsryeBYEkmk4pGo65+Zv+1ek6xew2gX+7Ccfdbp13/TD8EQdb+DeR0+LNbUEKh1+Gv3CpgIpFQdXW1TbNBELAGEAAAwDAEQMBBd7912pPqmx9QffOX+5ZeFbjqnxS8iqVXvK5AIngIgAgELs+CYhFAPxT0EOXn8Hp46nWELwQSARAAAMAw/vyTCsgiVQV0a0OIne5+67RvNoS4ybS7hEz7qy/nfG3rE990cSbh1L8K6PUGEb9V/rgsDIpBAETg3NL5vUCGQC/4Zf2h25eHcbP9my/0ZRtHELTPfUuv8jwEAkFFCxgAAMAwVAABl6SqcSa2gt3i9saPQqt/ud7jRjXQr5sn7JL6/agEfoj7BKMQVAABl/mlLeu2ziPb0w+nju+WaX/15ZLCX7bjwB5hD7qA3QiAAAAAhuFPJkDuX2fQjXawnyuNdu4ODkLbt5DjsTkEgJsIgDAeF5n2Tv/wVmgg5CLPAFAeWsAAAACGoQKIQLLrotBeV/+cuEC0n1u/g8lVEaTih8GwCxgoDgEQgVbqRaG9Dn5OCXL4y4bgBwDOoAWMwLul83vpR6Hj/eTut07bEtzCFv5M49QlYaiMAciGAAgAAGAYWsAIFbeqe49+/1ODjlk971BRx8xWwcu1PpBqH/ABKpxAaQiAQIEKCX25xhcbBlMIerDDhrUXuVOGAz627agOT73O62kMwG3gUAhawAAAAIbhT0IAKAD37QUQJlQAgQIU2/61+/1AuTasvch6OQd8bNtR37Rc/TQX+B8BEAAAwDC0gAGXpKqApW4IQbhtfeKbXk8hcPxU0exbefPjxhCgPwIgkAetW6SkAlrQ1wKGYUewn4JfNl7sDqb1i2LRAgYAADBMsP8MBADAh9xoCVP1QzkIgABQhK1PfNPWNrAXa/9SLdQgtoL93v7NJltQKyUUEvhgJ1rAAAAAhgnen3+AS9gAAqd5vfM3aBtCglj9y4VqHrwWnP/lA4BPhGVHsJQZqvwYBsMU+gA/oQUMAABgGP/9uQcAAVHqhhCvW7+5FFJtc6pKSKUPcBcBEHAJdwAJpzC1gwthZ0gk9AHeiViWZXk9CQRLMplUNBr1ehqOs3MTCOHPPP0DoV+rfoAkJRIJVVdXez0NuIg1gAAAAIahAoiimVIBlOyrAlIBBOBnVADNQwUQAADAMARAAAAAw9ACRtFMagGnlNoKpvULIAhoAZuHCmBAtLe366abblJVVZVGjRqlOXPm6PDhwxljFi5cqEgkkvG4+eabM8b09PRoyZIlqq2t1bBhwzR79mydPHnSzV8lkEoJcoQ/AIBfEQADYseOHVq0aJH27Nmjjo4OXbx4US0tLTp37lzGuDvvvFOdnZ3px5YtWzJeb21t1aZNm7Rx40bt2rVLZ8+e1cyZM3Xp0iU3fx0AAOAhWsAB9bvf/U6jRo3Sjh07NGnSJEkfVADfffddbd68Oet7EomERo4cqeeff17z58+XJL399tsaM2aMtmzZojvuuKOgzzaxBdzXYO1gKn8AgoYWsHm4E0hAJRIJSVJNTU3G89u3b9eoUaP0B3/wB5o8ebK+9rWvadSoUZKkffv26cKFC2ppaUmPj8fjampq0u7du3MGwJ6eHvX09KR/TiaTdv86gdI34KXCIKEPABAktIADyLIsLV26VLfeequamprSz8+YMUPf/e53tXXrVn3jG9/Q3r17NW3atHR46+rq0pAhQzR8+PCM49XV1amrqyvn57W3tysajaYfY8aMceYXAwAArqACGECLFy/Wq6++ql27dmU8n2rrSlJTU5NuvPFGNTQ06JVXXtHcuXNzHs+yLEUikZyvr1ixQkuXLk3/nEwmCYH/h8ofACCIqAAGzJIlS/Tyyy9r27ZtGj16dN6x9fX1amho0JEjRyRJsVhMvb29On36dMa47u5u1dXV5TxOZWWlqqurMx4AACC4CIABYVmWFi9erJdeeklbt25VY2PjoO85deqUTpw4ofr6eklSc3OzKioq1NHRkR7T2dmpgwcPauLEiY7NHQAA+Ast4IBYtGiRXnjhBf3whz9UVVVVes1eNBrV0KFDdfbsWbW1temuu+5SfX293njjDT366KOqra3V5z//+fTY+++/X8uWLdOIESNUU1Oj5cuXa9y4cZo+fXrBc2HjOACEC/+uG8hCIEjK+tiwYYNlWZb13nvvWS0tLdbIkSOtiooK69prr7UWLFhgHT9+POM458+ftxYvXmzV1NRYQ4cOtWbOnDlgzGD+53/+J+d8ePDgwYNH8B4nTpyw6/+uEBBcBxBFe/fddzV8+HAdP37c6OsB5pPaKHPixAnWTObAORoc5yg/zs/gBjtHlmXpzJkzisfjuuIKVoWZhBYwipb6RyIajfKP7iDYNDM4ztHgOEf5cX4Gl+8c8Ye8mYj7AAAAhiEAAgAAGIYAiKJVVlbqscceU2VlpddT8S3O0eA4R4PjHOXH+Rkc5wi5sAkEAADAMFQAAQAADEMABAAAMAwBEAAAwDAEQAAAAMMQAFG0J598Uo2Njbr66qvV3Nysn//8515PyRNtbW2KRCIZj1gsln7dsiy1tbUpHo9r6NChmjJlig4dOuThjJ23c+dOzZo1S/F4XJFIRJs3b854vZBz0tPToyVLlqi2tlbDhg3T7NmzdfLkSRd/C2cNdo4WLlw44Ht18803Z4wJ8zlqb2/XTTfdpKqqKo0aNUpz5szR4cOHM8aY/j0q5ByZ/j3C4AiAKMqLL76o1tZWrVy5Uvv379fnPvc5zZgxQ8ePH/d6ap741Kc+pc7OzvTjwIED6dfWrFmjtWvXav369dq7d69isZhuv/12nTlzxsMZO+vcuXMaP3681q9fn/X1Qs5Ja2urNm3apI0bN2rXrl06e/asZs6cqUuXLrn1azhqsHMkSXfeeWfG92rLli0Zr4f5HO3YsUOLFi3Snj171NHRoYsXL6qlpUXnzp1LjzH9e1TIOZLM/h6hAN7dhhhB9JnPfMZ68MEHM577+Mc/bn3lK1/xaEbeeeyxx6zx48dnfe3y5ctWLBazvv71r6efe//9961oNGr94z/+o0sz9JYka9OmTemfCzkn7777rlVRUWFt3LgxPeatt96yrrjiCuvHP/6xa3N3S/9zZFmWtWDBAuuP//iPc77HtHPU3d1tSbJ27NhhWRbfo2z6nyPL4nuEwVEBRMF6e3u1b98+tbS0ZDzf0tKi3bt3ezQrbx05ckTxeFyNjY26++67dfToUUnSsWPH1NXVlXGuKisrNXnyZGPPVSHnZN++fbpw4ULGmHg8rqamJqPO2/bt2zVq1Chdf/31+tKXvqTu7u70a6ado0QiIUmqqamRxPcom/7nKIXvEfIhAKJg77zzji5duqS6urqM5+vq6tTV1eXRrLwzYcIEPffcc/rJT36if/qnf1JXV5cmTpyoU6dOpc8H5+pDhZyTrq4uDRkyRMOHD885JuxmzJih7373u9q6dau+8Y1vaO/evZo2bZp6enokmXWOLMvS0qVLdeutt6qpqUkS36P+sp0jie8RBneV1xNA8EQikYyfLcsa8JwJZsyYkf7vcePG6ZZbbtEf/dEf6dlnn00vtuZcDVTKOTHpvM2fPz/9301NTbrxxhvV0NCgV155RXPnzs35vjCeo8WLF+vVV1/Vrl27BrzG9+gDuc4R3yMMhgogClZbW6srr7xywF+H3d3dA/4aN9GwYcM0btw4HTlyJL0bmHP1oULOSSwWU29vr06fPp1zjGnq6+vV0NCgI0eOSDLnHC1ZskQvv/yytm3bptGjR6ef53v0oVznKBtTv0fIjQCIgg0ZMkTNzc3q6OjIeL6jo0MTJ070aFb+0dPTo9dff1319fVqbGxULBbLOFe9vb3asWOHseeqkHPS3NysioqKjDGdnZ06ePCgseft1KlTOnHihOrr6yWF/xxZlqXFixfrpZde0tatW9XY2JjxOt+jwc9RNqZ9j1AAb/aeIKg2btxoVVRUWE8//bT12muvWa2trdawYcOsN954w+upuW7ZsmXW9u3braNHj1p79uyxZs6caVVVVaXPxde//nUrGo1aL730knXgwAHrnnvuserr661kMunxzJ1z5swZa//+/db+/fstSdbatWut/fv3W2+++aZlWYWdkwcffNAaPXq09bOf/cz61a9+ZU2bNs0aP368dfHiRa9+LVvlO0dnzpyxli1bZu3evds6duyYtW3bNuuWW26x/vAP/9CYc/QXf/EXVjQatbZv3251dnamH++99156jOnfo8HOEd8jFIIAiKJ961vfshoaGqwhQ4ZYN9xwQ8alB0wyf/58q76+3qqoqLDi8bg1d+5c69ChQ+nXL1++bD322GNWLBazKisrrUmTJlkHDhzwcMbO27ZtmyVpwGPBggWWZRV2Ts6fP28tXrzYqqmpsYYOHWrNnDnTOn78uAe/jTPynaP33nvPamlpsUaOHGlVVFRY1157rbVgwYIBv3+Yz1G2cyPJ2rBhQ3qM6d+jwc4R3yMUImJZluVevREAAABeYw0gAACAYQiAAAAAhiEAAgAAGIYACAAAYBgCIAAAgGEIgAAAAIYhAAIAABiGAAgAAGAYAiAAAIBhCIAAAACGIQACAAAYhgAIAABgGAIgAACAYQiAAAAAhiEAAgAAGIYACAAAYBgCIAAAgGEIgAAAAIYhAAIAABiGAAgAAGAYAiAAAIBhCIAAAACGIQACAAAYhgAIAABgGAIgAACAYQiAAAAAhiEAAgAAGIYACAAAYBgCIAAAgGEIgAAAAIYhAAIAABjm/wPyyP4IZRLwvwAAAABJRU5ErkJggg==\"></img>\n",
       "</td>\n",
       "<td style=\"text-align: center; vertical-align: top;\">\n",
       "\n",
       "<table>\n",
       "<tr><td>shape</td><td>(254,&nbsp;256)</td></tr>\n",
       "<tr><td>dtype</td><td>uint32</td></tr>\n",
       "<tr><td>size</td><td>254.0 kB</td></tr>\n",
       "<tr><td>min</td><td>0</td></tr><tr><td>max</td><td>44</td></tr>\n",
       "</table>\n",
       "\n",
       "</td>\n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "StackViewNDArray([[0, 0, 0, ..., 0, 0, 0],\n",
       "                  [0, 0, 0, ..., 0, 0, 0],\n",
       "                  [0, 0, 0, ..., 0, 0, 0],\n",
       "                  ...,\n",
       "                  [0, 0, 0, ..., 0, 0, 0],\n",
       "                  [0, 0, 0, ..., 0, 0, 0],\n",
       "                  [0, 0, 0, ..., 0, 0, 0]], dtype=uint32)"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "image = imread(\"data/blobs.tif\")\n",
    "labels = label(image > 120)\n",
    "labels = cle.exclude_small_labels(labels, maximum_size=4)\n",
    "labels = cle.exclude_labels_on_edges(labels)\n",
    "stackview.insight(labels)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "43c2e702-d540-4434-9ba0-e5db4d0618b6",
   "metadata": {},
   "source": [
    "As we want to differentiate objects using quantitative measurements, we extract those like this."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "ab4445a7-4593-4e9c-9d58-4fb156fcb49c",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>label</th>\n",
       "      <th>area</th>\n",
       "      <th>bbox_area</th>\n",
       "      <th>equivalent_diameter</th>\n",
       "      <th>convex_area</th>\n",
       "      <th>max_intensity</th>\n",
       "      <th>mean_intensity</th>\n",
       "      <th>min_intensity</th>\n",
       "      <th>extent</th>\n",
       "      <th>local_centroid-0</th>\n",
       "      <th>...</th>\n",
       "      <th>solidity</th>\n",
       "      <th>feret_diameter_max</th>\n",
       "      <th>major_axis_length</th>\n",
       "      <th>minor_axis_length</th>\n",
       "      <th>orientation</th>\n",
       "      <th>eccentricity</th>\n",
       "      <th>standard_deviation_intensity</th>\n",
       "      <th>aspect_ratio</th>\n",
       "      <th>roundness</th>\n",
       "      <th>circularity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>285.0</td>\n",
       "      <td>378.0</td>\n",
       "      <td>19.049233</td>\n",
       "      <td>295.0</td>\n",
       "      <td>248.0</td>\n",
       "      <td>204.294737</td>\n",
       "      <td>128.0</td>\n",
       "      <td>0.753968</td>\n",
       "      <td>9.757895</td>\n",
       "      <td>...</td>\n",
       "      <td>0.966102</td>\n",
       "      <td>21.840330</td>\n",
       "      <td>20.506730</td>\n",
       "      <td>17.770536</td>\n",
       "      <td>-0.344111</td>\n",
       "      <td>0.499054</td>\n",
       "      <td>36.500576</td>\n",
       "      <td>1.153974</td>\n",
       "      <td>0.862903</td>\n",
       "      <td>0.958941</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>81.0</td>\n",
       "      <td>99.0</td>\n",
       "      <td>10.155413</td>\n",
       "      <td>86.0</td>\n",
       "      <td>200.0</td>\n",
       "      <td>161.481481</td>\n",
       "      <td>128.0</td>\n",
       "      <td>0.818182</td>\n",
       "      <td>4.777778</td>\n",
       "      <td>...</td>\n",
       "      <td>0.941860</td>\n",
       "      <td>12.083046</td>\n",
       "      <td>11.153514</td>\n",
       "      <td>9.239435</td>\n",
       "      <td>-0.369542</td>\n",
       "      <td>0.560155</td>\n",
       "      <td>19.357017</td>\n",
       "      <td>1.207164</td>\n",
       "      <td>0.829033</td>\n",
       "      <td>1.038068</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>278.0</td>\n",
       "      <td>374.0</td>\n",
       "      <td>18.813840</td>\n",
       "      <td>297.0</td>\n",
       "      <td>224.0</td>\n",
       "      <td>174.848921</td>\n",
       "      <td>128.0</td>\n",
       "      <td>0.743316</td>\n",
       "      <td>10.794964</td>\n",
       "      <td>...</td>\n",
       "      <td>0.936027</td>\n",
       "      <td>23.086793</td>\n",
       "      <td>22.298284</td>\n",
       "      <td>16.037201</td>\n",
       "      <td>-0.084591</td>\n",
       "      <td>0.694790</td>\n",
       "      <td>25.426411</td>\n",
       "      <td>1.390410</td>\n",
       "      <td>0.711889</td>\n",
       "      <td>0.883826</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>231.0</td>\n",
       "      <td>288.0</td>\n",
       "      <td>17.149879</td>\n",
       "      <td>243.0</td>\n",
       "      <td>240.0</td>\n",
       "      <td>188.467532</td>\n",
       "      <td>128.0</td>\n",
       "      <td>0.802083</td>\n",
       "      <td>8.090909</td>\n",
       "      <td>...</td>\n",
       "      <td>0.950617</td>\n",
       "      <td>19.313208</td>\n",
       "      <td>18.345742</td>\n",
       "      <td>16.073821</td>\n",
       "      <td>-0.174639</td>\n",
       "      <td>0.482019</td>\n",
       "      <td>30.533319</td>\n",
       "      <td>1.141343</td>\n",
       "      <td>0.873879</td>\n",
       "      <td>0.972748</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>501.0</td>\n",
       "      <td>650.0</td>\n",
       "      <td>25.256544</td>\n",
       "      <td>518.0</td>\n",
       "      <td>232.0</td>\n",
       "      <td>189.141717</td>\n",
       "      <td>128.0</td>\n",
       "      <td>0.770769</td>\n",
       "      <td>12.191617</td>\n",
       "      <td>...</td>\n",
       "      <td>0.967181</td>\n",
       "      <td>27.513633</td>\n",
       "      <td>26.232105</td>\n",
       "      <td>24.403675</td>\n",
       "      <td>-0.316746</td>\n",
       "      <td>0.366805</td>\n",
       "      <td>25.456900</td>\n",
       "      <td>1.074924</td>\n",
       "      <td>0.927004</td>\n",
       "      <td>0.945559</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   label   area  bbox_area  equivalent_diameter  convex_area  max_intensity  \\\n",
       "0      1  285.0      378.0            19.049233        295.0          248.0   \n",
       "1      2   81.0       99.0            10.155413         86.0          200.0   \n",
       "2      3  278.0      374.0            18.813840        297.0          224.0   \n",
       "3      4  231.0      288.0            17.149879        243.0          240.0   \n",
       "4      5  501.0      650.0            25.256544        518.0          232.0   \n",
       "\n",
       "   mean_intensity  min_intensity    extent  local_centroid-0  ...  solidity  \\\n",
       "0      204.294737          128.0  0.753968          9.757895  ...  0.966102   \n",
       "1      161.481481          128.0  0.818182          4.777778  ...  0.941860   \n",
       "2      174.848921          128.0  0.743316         10.794964  ...  0.936027   \n",
       "3      188.467532          128.0  0.802083          8.090909  ...  0.950617   \n",
       "4      189.141717          128.0  0.770769         12.191617  ...  0.967181   \n",
       "\n",
       "   feret_diameter_max  major_axis_length  minor_axis_length  orientation  \\\n",
       "0           21.840330          20.506730          17.770536    -0.344111   \n",
       "1           12.083046          11.153514           9.239435    -0.369542   \n",
       "2           23.086793          22.298284          16.037201    -0.084591   \n",
       "3           19.313208          18.345742          16.073821    -0.174639   \n",
       "4           27.513633          26.232105          24.403675    -0.316746   \n",
       "\n",
       "   eccentricity  standard_deviation_intensity  aspect_ratio  roundness  \\\n",
       "0      0.499054                     36.500576      1.153974   0.862903   \n",
       "1      0.560155                     19.357017      1.207164   0.829033   \n",
       "2      0.694790                     25.426411      1.390410   0.711889   \n",
       "3      0.482019                     30.533319      1.141343   0.873879   \n",
       "4      0.366805                     25.456900      1.074924   0.927004   \n",
       "\n",
       "   circularity  \n",
       "0     0.958941  \n",
       "1     1.038068  \n",
       "2     0.883826  \n",
       "3     0.972748  \n",
       "4     0.945559  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "measurements = regionprops_table(image, labels, intensity=True, size=True, shape=True)\n",
    "measurements.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "c31f8bea-2b2a-4834-8a5b-430ebd12f9db",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>label</th>\n",
       "      <td>44.0</td>\n",
       "      <td>22.500000</td>\n",
       "      <td>12.845233</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>11.750000</td>\n",
       "      <td>22.500000</td>\n",
       "      <td>33.250000</td>\n",
       "      <td>44.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>area</th>\n",
       "      <td>44.0</td>\n",
       "      <td>401.863636</td>\n",
       "      <td>202.852288</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>242.750000</td>\n",
       "      <td>406.500000</td>\n",
       "      <td>547.500000</td>\n",
       "      <td>902.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bbox_area</th>\n",
       "      <td>44.0</td>\n",
       "      <td>542.750000</td>\n",
       "      <td>295.106376</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>318.250000</td>\n",
       "      <td>528.500000</td>\n",
       "      <td>702.000000</td>\n",
       "      <td>1350.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>equivalent_diameter</th>\n",
       "      <td>44.0</td>\n",
       "      <td>21.781085</td>\n",
       "      <td>6.174086</td>\n",
       "      <td>4.222008</td>\n",
       "      <td>17.580464</td>\n",
       "      <td>22.749685</td>\n",
       "      <td>26.402420</td>\n",
       "      <td>33.888967</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>convex_area</th>\n",
       "      <td>44.0</td>\n",
       "      <td>423.295455</td>\n",
       "      <td>216.613747</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>254.750000</td>\n",
       "      <td>427.500000</td>\n",
       "      <td>565.750000</td>\n",
       "      <td>981.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max_intensity</th>\n",
       "      <td>44.0</td>\n",
       "      <td>234.909091</td>\n",
       "      <td>17.517856</td>\n",
       "      <td>152.000000</td>\n",
       "      <td>232.000000</td>\n",
       "      <td>240.000000</td>\n",
       "      <td>248.000000</td>\n",
       "      <td>248.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean_intensity</th>\n",
       "      <td>44.0</td>\n",
       "      <td>190.116971</td>\n",
       "      <td>15.034153</td>\n",
       "      <td>138.857143</td>\n",
       "      <td>182.815316</td>\n",
       "      <td>190.688843</td>\n",
       "      <td>199.153996</td>\n",
       "      <td>219.915171</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min_intensity</th>\n",
       "      <td>44.0</td>\n",
       "      <td>128.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>128.000000</td>\n",
       "      <td>128.000000</td>\n",
       "      <td>128.000000</td>\n",
       "      <td>128.000000</td>\n",
       "      <td>128.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>extent</th>\n",
       "      <td>44.0</td>\n",
       "      <td>0.758804</td>\n",
       "      <td>0.063276</td>\n",
       "      <td>0.551039</td>\n",
       "      <td>0.751693</td>\n",
       "      <td>0.770000</td>\n",
       "      <td>0.787927</td>\n",
       "      <td>0.875000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>local_centroid-0</th>\n",
       "      <td>44.0</td>\n",
       "      <td>11.439824</td>\n",
       "      <td>4.126230</td>\n",
       "      <td>1.285714</td>\n",
       "      <td>8.642122</td>\n",
       "      <td>10.946602</td>\n",
       "      <td>13.254249</td>\n",
       "      <td>25.144124</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>local_centroid-1</th>\n",
       "      <td>44.0</td>\n",
       "      <td>10.138666</td>\n",
       "      <td>3.491815</td>\n",
       "      <td>1.500000</td>\n",
       "      <td>7.998087</td>\n",
       "      <td>10.561058</td>\n",
       "      <td>12.451894</td>\n",
       "      <td>19.291375</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>solidity</th>\n",
       "      <td>44.0</td>\n",
       "      <td>0.953153</td>\n",
       "      <td>0.024749</td>\n",
       "      <td>0.866477</td>\n",
       "      <td>0.948846</td>\n",
       "      <td>0.961628</td>\n",
       "      <td>0.966970</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>feret_diameter_max</th>\n",
       "      <td>44.0</td>\n",
       "      <td>26.382434</td>\n",
       "      <td>8.915046</td>\n",
       "      <td>4.472136</td>\n",
       "      <td>20.167557</td>\n",
       "      <td>26.112023</td>\n",
       "      <td>31.231386</td>\n",
       "      <td>52.201533</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>major_axis_length</th>\n",
       "      <td>44.0</td>\n",
       "      <td>25.876797</td>\n",
       "      <td>9.591558</td>\n",
       "      <td>4.208834</td>\n",
       "      <td>19.176806</td>\n",
       "      <td>25.307650</td>\n",
       "      <td>30.317106</td>\n",
       "      <td>54.785426</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>minor_axis_length</th>\n",
       "      <td>44.0</td>\n",
       "      <td>18.872898</td>\n",
       "      <td>5.158791</td>\n",
       "      <td>4.120630</td>\n",
       "      <td>16.021275</td>\n",
       "      <td>18.708410</td>\n",
       "      <td>23.092994</td>\n",
       "      <td>27.389304</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>orientation</th>\n",
       "      <td>44.0</td>\n",
       "      <td>0.053057</td>\n",
       "      <td>0.691430</td>\n",
       "      <td>-1.263830</td>\n",
       "      <td>-0.335619</td>\n",
       "      <td>-0.186244</td>\n",
       "      <td>0.182000</td>\n",
       "      <td>1.570796</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>eccentricity</th>\n",
       "      <td>44.0</td>\n",
       "      <td>0.600434</td>\n",
       "      <td>0.165688</td>\n",
       "      <td>0.203653</td>\n",
       "      <td>0.489504</td>\n",
       "      <td>0.588190</td>\n",
       "      <td>0.680256</td>\n",
       "      <td>0.931725</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>standard_deviation_intensity</th>\n",
       "      <td>44.0</td>\n",
       "      <td>29.556705</td>\n",
       "      <td>5.507399</td>\n",
       "      <td>8.870957</td>\n",
       "      <td>27.556078</td>\n",
       "      <td>29.650398</td>\n",
       "      <td>32.813644</td>\n",
       "      <td>38.447968</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>aspect_ratio</th>\n",
       "      <td>44.0</td>\n",
       "      <td>1.374342</td>\n",
       "      <td>0.397611</td>\n",
       "      <td>1.021406</td>\n",
       "      <td>1.146802</td>\n",
       "      <td>1.236720</td>\n",
       "      <td>1.364368</td>\n",
       "      <td>2.753581</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>roundness</th>\n",
       "      <td>44.0</td>\n",
       "      <td>0.762889</td>\n",
       "      <td>0.156695</td>\n",
       "      <td>0.337027</td>\n",
       "      <td>0.732598</td>\n",
       "      <td>0.805053</td>\n",
       "      <td>0.867417</td>\n",
       "      <td>1.006270</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>circularity</th>\n",
       "      <td>44.0</td>\n",
       "      <td>0.918858</td>\n",
       "      <td>0.133288</td>\n",
       "      <td>0.610180</td>\n",
       "      <td>0.900136</td>\n",
       "      <td>0.939080</td>\n",
       "      <td>0.959653</td>\n",
       "      <td>1.500400</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                              count        mean         std         min  \\\n",
       "label                          44.0   22.500000   12.845233    1.000000   \n",
       "area                           44.0  401.863636  202.852288   14.000000   \n",
       "bbox_area                      44.0  542.750000  295.106376   16.000000   \n",
       "equivalent_diameter            44.0   21.781085    6.174086    4.222008   \n",
       "convex_area                    44.0  423.295455  216.613747   14.000000   \n",
       "max_intensity                  44.0  234.909091   17.517856  152.000000   \n",
       "mean_intensity                 44.0  190.116971   15.034153  138.857143   \n",
       "min_intensity                  44.0  128.000000    0.000000  128.000000   \n",
       "extent                         44.0    0.758804    0.063276    0.551039   \n",
       "local_centroid-0               44.0   11.439824    4.126230    1.285714   \n",
       "local_centroid-1               44.0   10.138666    3.491815    1.500000   \n",
       "solidity                       44.0    0.953153    0.024749    0.866477   \n",
       "feret_diameter_max             44.0   26.382434    8.915046    4.472136   \n",
       "major_axis_length              44.0   25.876797    9.591558    4.208834   \n",
       "minor_axis_length              44.0   18.872898    5.158791    4.120630   \n",
       "orientation                    44.0    0.053057    0.691430   -1.263830   \n",
       "eccentricity                   44.0    0.600434    0.165688    0.203653   \n",
       "standard_deviation_intensity   44.0   29.556705    5.507399    8.870957   \n",
       "aspect_ratio                   44.0    1.374342    0.397611    1.021406   \n",
       "roundness                      44.0    0.762889    0.156695    0.337027   \n",
       "circularity                    44.0    0.918858    0.133288    0.610180   \n",
       "\n",
       "                                     25%         50%         75%          max  \n",
       "label                          11.750000   22.500000   33.250000    44.000000  \n",
       "area                          242.750000  406.500000  547.500000   902.000000  \n",
       "bbox_area                     318.250000  528.500000  702.000000  1350.000000  \n",
       "equivalent_diameter            17.580464   22.749685   26.402420    33.888967  \n",
       "convex_area                   254.750000  427.500000  565.750000   981.000000  \n",
       "max_intensity                 232.000000  240.000000  248.000000   248.000000  \n",
       "mean_intensity                182.815316  190.688843  199.153996   219.915171  \n",
       "min_intensity                 128.000000  128.000000  128.000000   128.000000  \n",
       "extent                          0.751693    0.770000    0.787927     0.875000  \n",
       "local_centroid-0                8.642122   10.946602   13.254249    25.144124  \n",
       "local_centroid-1                7.998087   10.561058   12.451894    19.291375  \n",
       "solidity                        0.948846    0.961628    0.966970     1.000000  \n",
       "feret_diameter_max             20.167557   26.112023   31.231386    52.201533  \n",
       "major_axis_length              19.176806   25.307650   30.317106    54.785426  \n",
       "minor_axis_length              16.021275   18.708410   23.092994    27.389304  \n",
       "orientation                    -0.335619   -0.186244    0.182000     1.570796  \n",
       "eccentricity                    0.489504    0.588190    0.680256     0.931725  \n",
       "standard_deviation_intensity   27.556078   29.650398   32.813644    38.447968  \n",
       "aspect_ratio                    1.146802    1.236720    1.364368     2.753581  \n",
       "roundness                       0.732598    0.805053    0.867417     1.006270  \n",
       "circularity                     0.900136    0.939080    0.959653     1.500400  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "measurements.describe().T"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "340418ca-82bd-47ac-b5a6-7bdf9d87436d",
   "metadata": {},
   "source": [
    "## Cleaning data\n",
    "In the following we replace `inf` and `nan` values with zeros. This is partially dangerous, because will will make average measurements misleading. It is however necessary so that we can apply the UMAP algorithm to the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "67ce3645-4b2d-4c3e-80d9-350c3e04b981",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "measurements = measurements.replace([np.inf, -np.inf, np.nan], 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "f52b8a11-a97f-4905-9269-14884fc0357b",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DataFrame contains NaNs: False\n"
     ]
    }
   ],
   "source": [
    "print(\"DataFrame contains NaNs:\", measurements.isna().any().any())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "97953948-5ed2-430b-a85e-8037b46fccde",
   "metadata": {},
   "source": [
    "These are all columns in the data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "1c91d5ea-28ae-40ca-af9c-4c490e07bbb3",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['label', 'area', 'bbox_area', 'equivalent_diameter', 'convex_area',\n",
       "       'max_intensity', 'mean_intensity', 'min_intensity', 'extent',\n",
       "       'local_centroid-0', 'local_centroid-1', 'solidity',\n",
       "       'feret_diameter_max', 'major_axis_length', 'minor_axis_length',\n",
       "       'orientation', 'eccentricity', 'standard_deviation_intensity',\n",
       "       'aspect_ratio', 'roundness', 'circularity'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "measurements.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "03091fb5-daa4-462b-9c61-2ed386da4c16",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>label</th>\n",
       "      <td>44.0</td>\n",
       "      <td>22.500000</td>\n",
       "      <td>12.845233</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>11.750000</td>\n",
       "      <td>22.500000</td>\n",
       "      <td>33.250000</td>\n",
       "      <td>44.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>area</th>\n",
       "      <td>44.0</td>\n",
       "      <td>401.863636</td>\n",
       "      <td>202.852288</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>242.750000</td>\n",
       "      <td>406.500000</td>\n",
       "      <td>547.500000</td>\n",
       "      <td>902.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bbox_area</th>\n",
       "      <td>44.0</td>\n",
       "      <td>542.750000</td>\n",
       "      <td>295.106376</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>318.250000</td>\n",
       "      <td>528.500000</td>\n",
       "      <td>702.000000</td>\n",
       "      <td>1350.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>equivalent_diameter</th>\n",
       "      <td>44.0</td>\n",
       "      <td>21.781085</td>\n",
       "      <td>6.174086</td>\n",
       "      <td>4.222008</td>\n",
       "      <td>17.580464</td>\n",
       "      <td>22.749685</td>\n",
       "      <td>26.402420</td>\n",
       "      <td>33.888967</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>convex_area</th>\n",
       "      <td>44.0</td>\n",
       "      <td>423.295455</td>\n",
       "      <td>216.613747</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>254.750000</td>\n",
       "      <td>427.500000</td>\n",
       "      <td>565.750000</td>\n",
       "      <td>981.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max_intensity</th>\n",
       "      <td>44.0</td>\n",
       "      <td>234.909091</td>\n",
       "      <td>17.517856</td>\n",
       "      <td>152.000000</td>\n",
       "      <td>232.000000</td>\n",
       "      <td>240.000000</td>\n",
       "      <td>248.000000</td>\n",
       "      <td>248.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean_intensity</th>\n",
       "      <td>44.0</td>\n",
       "      <td>190.116971</td>\n",
       "      <td>15.034153</td>\n",
       "      <td>138.857143</td>\n",
       "      <td>182.815316</td>\n",
       "      <td>190.688843</td>\n",
       "      <td>199.153996</td>\n",
       "      <td>219.915171</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min_intensity</th>\n",
       "      <td>44.0</td>\n",
       "      <td>128.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>128.000000</td>\n",
       "      <td>128.000000</td>\n",
       "      <td>128.000000</td>\n",
       "      <td>128.000000</td>\n",
       "      <td>128.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>extent</th>\n",
       "      <td>44.0</td>\n",
       "      <td>0.758804</td>\n",
       "      <td>0.063276</td>\n",
       "      <td>0.551039</td>\n",
       "      <td>0.751693</td>\n",
       "      <td>0.770000</td>\n",
       "      <td>0.787927</td>\n",
       "      <td>0.875000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>local_centroid-0</th>\n",
       "      <td>44.0</td>\n",
       "      <td>11.439824</td>\n",
       "      <td>4.126230</td>\n",
       "      <td>1.285714</td>\n",
       "      <td>8.642122</td>\n",
       "      <td>10.946602</td>\n",
       "      <td>13.254249</td>\n",
       "      <td>25.144124</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>local_centroid-1</th>\n",
       "      <td>44.0</td>\n",
       "      <td>10.138666</td>\n",
       "      <td>3.491815</td>\n",
       "      <td>1.500000</td>\n",
       "      <td>7.998087</td>\n",
       "      <td>10.561058</td>\n",
       "      <td>12.451894</td>\n",
       "      <td>19.291375</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>solidity</th>\n",
       "      <td>44.0</td>\n",
       "      <td>0.953153</td>\n",
       "      <td>0.024749</td>\n",
       "      <td>0.866477</td>\n",
       "      <td>0.948846</td>\n",
       "      <td>0.961628</td>\n",
       "      <td>0.966970</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>feret_diameter_max</th>\n",
       "      <td>44.0</td>\n",
       "      <td>26.382434</td>\n",
       "      <td>8.915046</td>\n",
       "      <td>4.472136</td>\n",
       "      <td>20.167557</td>\n",
       "      <td>26.112023</td>\n",
       "      <td>31.231386</td>\n",
       "      <td>52.201533</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>major_axis_length</th>\n",
       "      <td>44.0</td>\n",
       "      <td>25.876797</td>\n",
       "      <td>9.591558</td>\n",
       "      <td>4.208834</td>\n",
       "      <td>19.176806</td>\n",
       "      <td>25.307650</td>\n",
       "      <td>30.317106</td>\n",
       "      <td>54.785426</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>minor_axis_length</th>\n",
       "      <td>44.0</td>\n",
       "      <td>18.872898</td>\n",
       "      <td>5.158791</td>\n",
       "      <td>4.120630</td>\n",
       "      <td>16.021275</td>\n",
       "      <td>18.708410</td>\n",
       "      <td>23.092994</td>\n",
       "      <td>27.389304</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>orientation</th>\n",
       "      <td>44.0</td>\n",
       "      <td>0.053057</td>\n",
       "      <td>0.691430</td>\n",
       "      <td>-1.263830</td>\n",
       "      <td>-0.335619</td>\n",
       "      <td>-0.186244</td>\n",
       "      <td>0.182000</td>\n",
       "      <td>1.570796</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>eccentricity</th>\n",
       "      <td>44.0</td>\n",
       "      <td>0.600434</td>\n",
       "      <td>0.165688</td>\n",
       "      <td>0.203653</td>\n",
       "      <td>0.489504</td>\n",
       "      <td>0.588190</td>\n",
       "      <td>0.680256</td>\n",
       "      <td>0.931725</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>standard_deviation_intensity</th>\n",
       "      <td>44.0</td>\n",
       "      <td>29.556705</td>\n",
       "      <td>5.507399</td>\n",
       "      <td>8.870957</td>\n",
       "      <td>27.556078</td>\n",
       "      <td>29.650398</td>\n",
       "      <td>32.813644</td>\n",
       "      <td>38.447968</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>aspect_ratio</th>\n",
       "      <td>44.0</td>\n",
       "      <td>1.374342</td>\n",
       "      <td>0.397611</td>\n",
       "      <td>1.021406</td>\n",
       "      <td>1.146802</td>\n",
       "      <td>1.236720</td>\n",
       "      <td>1.364368</td>\n",
       "      <td>2.753581</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>roundness</th>\n",
       "      <td>44.0</td>\n",
       "      <td>0.762889</td>\n",
       "      <td>0.156695</td>\n",
       "      <td>0.337027</td>\n",
       "      <td>0.732598</td>\n",
       "      <td>0.805053</td>\n",
       "      <td>0.867417</td>\n",
       "      <td>1.006270</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>circularity</th>\n",
       "      <td>44.0</td>\n",
       "      <td>0.918858</td>\n",
       "      <td>0.133288</td>\n",
       "      <td>0.610180</td>\n",
       "      <td>0.900136</td>\n",
       "      <td>0.939080</td>\n",
       "      <td>0.959653</td>\n",
       "      <td>1.500400</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                              count        mean         std         min  \\\n",
       "label                          44.0   22.500000   12.845233    1.000000   \n",
       "area                           44.0  401.863636  202.852288   14.000000   \n",
       "bbox_area                      44.0  542.750000  295.106376   16.000000   \n",
       "equivalent_diameter            44.0   21.781085    6.174086    4.222008   \n",
       "convex_area                    44.0  423.295455  216.613747   14.000000   \n",
       "max_intensity                  44.0  234.909091   17.517856  152.000000   \n",
       "mean_intensity                 44.0  190.116971   15.034153  138.857143   \n",
       "min_intensity                  44.0  128.000000    0.000000  128.000000   \n",
       "extent                         44.0    0.758804    0.063276    0.551039   \n",
       "local_centroid-0               44.0   11.439824    4.126230    1.285714   \n",
       "local_centroid-1               44.0   10.138666    3.491815    1.500000   \n",
       "solidity                       44.0    0.953153    0.024749    0.866477   \n",
       "feret_diameter_max             44.0   26.382434    8.915046    4.472136   \n",
       "major_axis_length              44.0   25.876797    9.591558    4.208834   \n",
       "minor_axis_length              44.0   18.872898    5.158791    4.120630   \n",
       "orientation                    44.0    0.053057    0.691430   -1.263830   \n",
       "eccentricity                   44.0    0.600434    0.165688    0.203653   \n",
       "standard_deviation_intensity   44.0   29.556705    5.507399    8.870957   \n",
       "aspect_ratio                   44.0    1.374342    0.397611    1.021406   \n",
       "roundness                      44.0    0.762889    0.156695    0.337027   \n",
       "circularity                    44.0    0.918858    0.133288    0.610180   \n",
       "\n",
       "                                     25%         50%         75%          max  \n",
       "label                          11.750000   22.500000   33.250000    44.000000  \n",
       "area                          242.750000  406.500000  547.500000   902.000000  \n",
       "bbox_area                     318.250000  528.500000  702.000000  1350.000000  \n",
       "equivalent_diameter            17.580464   22.749685   26.402420    33.888967  \n",
       "convex_area                   254.750000  427.500000  565.750000   981.000000  \n",
       "max_intensity                 232.000000  240.000000  248.000000   248.000000  \n",
       "mean_intensity                182.815316  190.688843  199.153996   219.915171  \n",
       "min_intensity                 128.000000  128.000000  128.000000   128.000000  \n",
       "extent                          0.751693    0.770000    0.787927     0.875000  \n",
       "local_centroid-0                8.642122   10.946602   13.254249    25.144124  \n",
       "local_centroid-1                7.998087   10.561058   12.451894    19.291375  \n",
       "solidity                        0.948846    0.961628    0.966970     1.000000  \n",
       "feret_diameter_max             20.167557   26.112023   31.231386    52.201533  \n",
       "major_axis_length              19.176806   25.307650   30.317106    54.785426  \n",
       "minor_axis_length              16.021275   18.708410   23.092994    27.389304  \n",
       "orientation                    -0.335619   -0.186244    0.182000     1.570796  \n",
       "eccentricity                    0.489504    0.588190    0.680256     0.931725  \n",
       "standard_deviation_intensity   27.556078   29.650398   32.813644    38.447968  \n",
       "aspect_ratio                    1.146802    1.236720    1.364368     2.753581  \n",
       "roundness                       0.732598    0.805053    0.867417     1.006270  \n",
       "circularity                     0.900136    0.939080    0.959653     1.500400  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "measurements.describe().T"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aa7bf019-9a3b-4e22-959f-d2449fe3588c",
   "metadata": {},
   "source": [
    "## Column selection and dimensionality reduction\n",
    "We next select columns that are reasonable to differentiate objects and compute a UMAP from them."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "1a99d360-cb65-448c-a157-49978a0a8878",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>area</th>\n",
       "      <td>44.0</td>\n",
       "      <td>401.863636</td>\n",
       "      <td>202.852288</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>242.750000</td>\n",
       "      <td>406.500000</td>\n",
       "      <td>547.500000</td>\n",
       "      <td>902.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>equivalent_diameter</th>\n",
       "      <td>44.0</td>\n",
       "      <td>21.781085</td>\n",
       "      <td>6.174086</td>\n",
       "      <td>4.222008</td>\n",
       "      <td>17.580464</td>\n",
       "      <td>22.749685</td>\n",
       "      <td>26.402420</td>\n",
       "      <td>33.888967</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>convex_area</th>\n",
       "      <td>44.0</td>\n",
       "      <td>423.295455</td>\n",
       "      <td>216.613747</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>254.750000</td>\n",
       "      <td>427.500000</td>\n",
       "      <td>565.750000</td>\n",
       "      <td>981.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max_intensity</th>\n",
       "      <td>44.0</td>\n",
       "      <td>234.909091</td>\n",
       "      <td>17.517856</td>\n",
       "      <td>152.000000</td>\n",
       "      <td>232.000000</td>\n",
       "      <td>240.000000</td>\n",
       "      <td>248.000000</td>\n",
       "      <td>248.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean_intensity</th>\n",
       "      <td>44.0</td>\n",
       "      <td>190.116971</td>\n",
       "      <td>15.034153</td>\n",
       "      <td>138.857143</td>\n",
       "      <td>182.815316</td>\n",
       "      <td>190.688843</td>\n",
       "      <td>199.153996</td>\n",
       "      <td>219.915171</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min_intensity</th>\n",
       "      <td>44.0</td>\n",
       "      <td>128.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>128.000000</td>\n",
       "      <td>128.000000</td>\n",
       "      <td>128.000000</td>\n",
       "      <td>128.000000</td>\n",
       "      <td>128.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>extent</th>\n",
       "      <td>44.0</td>\n",
       "      <td>0.758804</td>\n",
       "      <td>0.063276</td>\n",
       "      <td>0.551039</td>\n",
       "      <td>0.751693</td>\n",
       "      <td>0.770000</td>\n",
       "      <td>0.787927</td>\n",
       "      <td>0.875000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>solidity</th>\n",
       "      <td>44.0</td>\n",
       "      <td>0.953153</td>\n",
       "      <td>0.024749</td>\n",
       "      <td>0.866477</td>\n",
       "      <td>0.948846</td>\n",
       "      <td>0.961628</td>\n",
       "      <td>0.966970</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>feret_diameter_max</th>\n",
       "      <td>44.0</td>\n",
       "      <td>26.382434</td>\n",
       "      <td>8.915046</td>\n",
       "      <td>4.472136</td>\n",
       "      <td>20.167557</td>\n",
       "      <td>26.112023</td>\n",
       "      <td>31.231386</td>\n",
       "      <td>52.201533</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>major_axis_length</th>\n",
       "      <td>44.0</td>\n",
       "      <td>25.876797</td>\n",
       "      <td>9.591558</td>\n",
       "      <td>4.208834</td>\n",
       "      <td>19.176806</td>\n",
       "      <td>25.307650</td>\n",
       "      <td>30.317106</td>\n",
       "      <td>54.785426</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>minor_axis_length</th>\n",
       "      <td>44.0</td>\n",
       "      <td>18.872898</td>\n",
       "      <td>5.158791</td>\n",
       "      <td>4.120630</td>\n",
       "      <td>16.021275</td>\n",
       "      <td>18.708410</td>\n",
       "      <td>23.092994</td>\n",
       "      <td>27.389304</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>eccentricity</th>\n",
       "      <td>44.0</td>\n",
       "      <td>0.600434</td>\n",
       "      <td>0.165688</td>\n",
       "      <td>0.203653</td>\n",
       "      <td>0.489504</td>\n",
       "      <td>0.588190</td>\n",
       "      <td>0.680256</td>\n",
       "      <td>0.931725</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>standard_deviation_intensity</th>\n",
       "      <td>44.0</td>\n",
       "      <td>29.556705</td>\n",
       "      <td>5.507399</td>\n",
       "      <td>8.870957</td>\n",
       "      <td>27.556078</td>\n",
       "      <td>29.650398</td>\n",
       "      <td>32.813644</td>\n",
       "      <td>38.447968</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>aspect_ratio</th>\n",
       "      <td>44.0</td>\n",
       "      <td>1.374342</td>\n",
       "      <td>0.397611</td>\n",
       "      <td>1.021406</td>\n",
       "      <td>1.146802</td>\n",
       "      <td>1.236720</td>\n",
       "      <td>1.364368</td>\n",
       "      <td>2.753581</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>roundness</th>\n",
       "      <td>44.0</td>\n",
       "      <td>0.762889</td>\n",
       "      <td>0.156695</td>\n",
       "      <td>0.337027</td>\n",
       "      <td>0.732598</td>\n",
       "      <td>0.805053</td>\n",
       "      <td>0.867417</td>\n",
       "      <td>1.006270</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>circularity</th>\n",
       "      <td>44.0</td>\n",
       "      <td>0.918858</td>\n",
       "      <td>0.133288</td>\n",
       "      <td>0.610180</td>\n",
       "      <td>0.900136</td>\n",
       "      <td>0.939080</td>\n",
       "      <td>0.959653</td>\n",
       "      <td>1.500400</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                              count        mean         std         min  \\\n",
       "area                           44.0  401.863636  202.852288   14.000000   \n",
       "equivalent_diameter            44.0   21.781085    6.174086    4.222008   \n",
       "convex_area                    44.0  423.295455  216.613747   14.000000   \n",
       "max_intensity                  44.0  234.909091   17.517856  152.000000   \n",
       "mean_intensity                 44.0  190.116971   15.034153  138.857143   \n",
       "min_intensity                  44.0  128.000000    0.000000  128.000000   \n",
       "extent                         44.0    0.758804    0.063276    0.551039   \n",
       "solidity                       44.0    0.953153    0.024749    0.866477   \n",
       "feret_diameter_max             44.0   26.382434    8.915046    4.472136   \n",
       "major_axis_length              44.0   25.876797    9.591558    4.208834   \n",
       "minor_axis_length              44.0   18.872898    5.158791    4.120630   \n",
       "eccentricity                   44.0    0.600434    0.165688    0.203653   \n",
       "standard_deviation_intensity   44.0   29.556705    5.507399    8.870957   \n",
       "aspect_ratio                   44.0    1.374342    0.397611    1.021406   \n",
       "roundness                      44.0    0.762889    0.156695    0.337027   \n",
       "circularity                    44.0    0.918858    0.133288    0.610180   \n",
       "\n",
       "                                     25%         50%         75%         max  \n",
       "area                          242.750000  406.500000  547.500000  902.000000  \n",
       "equivalent_diameter            17.580464   22.749685   26.402420   33.888967  \n",
       "convex_area                   254.750000  427.500000  565.750000  981.000000  \n",
       "max_intensity                 232.000000  240.000000  248.000000  248.000000  \n",
       "mean_intensity                182.815316  190.688843  199.153996  219.915171  \n",
       "min_intensity                 128.000000  128.000000  128.000000  128.000000  \n",
       "extent                          0.751693    0.770000    0.787927    0.875000  \n",
       "solidity                        0.948846    0.961628    0.966970    1.000000  \n",
       "feret_diameter_max             20.167557   26.112023   31.231386   52.201533  \n",
       "major_axis_length              19.176806   25.307650   30.317106   54.785426  \n",
       "minor_axis_length              16.021275   18.708410   23.092994   27.389304  \n",
       "eccentricity                    0.489504    0.588190    0.680256    0.931725  \n",
       "standard_deviation_intensity   27.556078   29.650398   32.813644   38.447968  \n",
       "aspect_ratio                    1.146802    1.236720    1.364368    2.753581  \n",
       "roundness                       0.732598    0.805053    0.867417    1.006270  \n",
       "circularity                     0.900136    0.939080    0.959653    1.500400  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "selected_measurements = measurements[[\n",
    "        'area', \n",
    "        'equivalent_diameter', \n",
    "        'convex_area',\n",
    "        'max_intensity', \n",
    "        'mean_intensity', \n",
    "        'min_intensity', \n",
    "        'extent',\n",
    "        'solidity',\n",
    "        'feret_diameter_max', \n",
    "        'major_axis_length', \n",
    "        'minor_axis_length',\n",
    "        'eccentricity', \n",
    "        'standard_deviation_intensity',\n",
    "        'aspect_ratio', \n",
    "        'roundness', \n",
    "        'circularity']]\n",
    "selected_measurements.describe().T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "3c9c31af-e052-4769-ab2e-75722fec8f4d",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# configure UMAP algorithm\n",
    "umap = UMAP(n_neighbors=5, n_components=2)\n",
    "\n",
    "# apply algorithm\n",
    "transformed_data = umap.fit_transform(selected_measurements.values.tolist())\n",
    "\n",
    "# store results back in table\n",
    "measurements['UMAP0'] = transformed_data[:,0]\n",
    "measurements['UMAP1'] = transformed_data[:,1]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3a36c6cf-19a6-4bba-85f4-7c22605c771b",
   "metadata": {},
   "source": [
    "This is how the UMAP looks like:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "8e930922-7298-474d-a6df-da7407a9765a",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "seaborn.scatterplot(measurements, x='UMAP0', y='UMAP1');"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bcf191ed-3c07-4e3f-8f15-948fb671552c",
   "metadata": {},
   "source": [
    "## Interactive visualization\n",
    "We store the data in napari for better visualization."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "461fa6c5-2202-4af5-81eb-59d529348523",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Invalid schema for package 'ome-types', please run 'npe2 validate ome-types' to check for manifest errors.\n"
     ]
    }
   ],
   "source": [
    "import napari\n",
    "viewer = napari.Viewer()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "27471a84-5003-4df3-b9d1-0d7dfcf07805",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "viewer.add_image(image)\n",
    "labels_layer = viewer.add_labels(labels)\n",
    "labels_layer.features = measurements"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e53cfecd-a6e6-4964-b2a8-5935a5eb5a3a",
   "metadata": {},
   "source": [
    "## REQUIRED INTERACTION\n",
    "Visualize it using the [napari clusters plotter](https://github.com/BiAPoL/napari-clusters-plotter) using the menu `Tools > Visualization > Plot measurements (ncp)`. In this tool we can use a lasso-annotation to identify the cluster that corresponds to the 8-shaped objects. Continue executing Jupyter notebook cells once you have a visualization that is similar to the screen shown below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "0c5ba7ba-52ba-435f-9abc-6374150ed1f1",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\" alt=\"\"></img>"
      ],
      "text/plain": [
       "<napari.utils.notebook_display.NotebookScreenshot at 0x2d58c1eaca0>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "napari.utils.nbscreenshot(viewer)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e0c313c2-7178-433f-bace-49647fc06e23",
   "metadata": {},
   "source": [
    "We next retrieve the annotation back from napari like this:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "f0df0770-3313-4b89-89d8-d3678cd39745",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0]\n"
     ]
    }
   ],
   "source": [
    "measurements = labels_layer.features\n",
    "print(measurements['MANUAL_CLUSTER_ID'].to_list())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "534b2593-0650-4535-bd1f-a56cc0b4ef73",
   "metadata": {},
   "source": [
    "## Correlation statistics\n",
    "We can then visualize a cross-correlation matrix of all parameters with each other, including our annotation. This facilitates to identify columns that allow differentiation of objects from others."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "b70588aa-4ccc-47db-b8da-3de8ff90375f",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
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       "  background-color: #000000;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_d9779_row0_col8, #T_d9779_row8_col0 {\n",
       "  background-color: #f6f7f5;\n",
       "  color: #000000;\n",
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       "#T_d9779_row0_col9, #T_d9779_row9_col0 {\n",
       "  background-color: #e9f5d8;\n",
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       "#T_d9779_row0_col10, #T_d9779_row10_col0 {\n",
       "  background-color: #e2f3ca;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_d9779_row0_col11, #T_d9779_row11_col0 {\n",
       "  background-color: #f1f6e8;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_d9779_row0_col12, #T_d9779_row12_col0 {\n",
       "  background-color: #e7f5d2;\n",
       "  color: #000000;\n",
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       "#T_d9779_row0_col13, #T_d9779_row3_col15, #T_d9779_row13_col0, #T_d9779_row15_col3 {\n",
       "  background-color: #e7f5d3;\n",
       "  color: #000000;\n",
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       "#T_d9779_row0_col15, #T_d9779_row10_col16, #T_d9779_row10_col18, #T_d9779_row15_col0, #T_d9779_row16_col10, #T_d9779_row18_col10, #T_d9779_row18_col22, #T_d9779_row22_col18 {\n",
       "  background-color: #cdeaa7;\n",
       "  color: #000000;\n",
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       "#T_d9779_row0_col16, #T_d9779_row16_col0 {\n",
       "  background-color: #f3f7ef;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_d9779_row0_col17, #T_d9779_row17_col0 {\n",
       "  background-color: #e8f5d5;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_d9779_row0_col18, #T_d9779_row18_col0 {\n",
       "  background-color: #f5f7f3;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_d9779_row0_col19, #T_d9779_row0_col20, #T_d9779_row8_col14, #T_d9779_row14_col8, #T_d9779_row17_col19, #T_d9779_row19_col0, #T_d9779_row19_col17, #T_d9779_row20_col0 {\n",
       "  background-color: #f9eef4;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_d9779_row0_col21, #T_d9779_row21_col0 {\n",
       "  background-color: #faedf3;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_d9779_row0_col22, #T_d9779_row22_col0 {\n",
       "  background-color: #edf6df;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_d9779_row0_col23, #T_d9779_row23_col0 {\n",
       "  background-color: #f1f6ea;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_d9779_row1_col2, #T_d9779_row2_col1, #T_d9779_row2_col12, #T_d9779_row3_col4, #T_d9779_row4_col3, #T_d9779_row12_col2 {\n",
       "  background-color: #2b691a;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_d9779_row1_col3, #T_d9779_row3_col1 {\n",
       "  background-color: #2a681a;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_d9779_row1_col5, #T_d9779_row5_col1 {\n",
       "  background-color: #9acd61;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_d9779_row1_col6, #T_d9779_row6_col1 {\n",
       "  background-color: #95cb5c;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_d9779_row1_col8, #T_d9779_row8_col1, #T_d9779_row10_col19, #T_d9779_row19_col10 {\n",
       "  background-color: #f2b8db;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_d9779_row1_col9, #T_d9779_row8_col11, #T_d9779_row9_col1, #T_d9779_row11_col8, #T_d9779_row16_col18, #T_d9779_row18_col16 {\n",
       "  background-color: #43861f;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_d9779_row1_col10, #T_d9779_row3_col12, #T_d9779_row4_col10, #T_d9779_row4_col13, #T_d9779_row9_col12, #T_d9779_row9_col13, #T_d9779_row10_col1, #T_d9779_row10_col4, #T_d9779_row12_col3, #T_d9779_row12_col9, #T_d9779_row13_col4, #T_d9779_row13_col9 {\n",
       "  background-color: #33721c;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_d9779_row1_col11, #T_d9779_row11_col1 {\n",
       "  background-color: #f9d1e8;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_d9779_row1_col12, #T_d9779_row12_col1 {\n",
       "  background-color: #34741c;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_d9779_row1_col13, #T_d9779_row13_col1 {\n",
       "  background-color: #37781c;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_d9779_row1_col14, #T_d9779_row3_col9, #T_d9779_row9_col3, #T_d9779_row14_col1 {\n",
       "  background-color: #42841f;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_d9779_row1_col15, #T_d9779_row4_col17, #T_d9779_row9_col17, #T_d9779_row13_col17, #T_d9779_row15_col1, #T_d9779_row17_col4, #T_d9779_row17_col9, #T_d9779_row17_col13 {\n",
       "  background-color: #d6eeb6;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_d9779_row1_col16, #T_d9779_row16_col1 {\n",
       "  background-color: #c7e89f;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_d9779_row1_col17, #T_d9779_row17_col1 {\n",
       "  background-color: #d4edb3;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_d9779_row1_col18, #T_d9779_row6_col9, #T_d9779_row9_col6, #T_d9779_row18_col1 {\n",
       "  background-color: #b7e085;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_d9779_row1_col19, #T_d9779_row2_col11, #T_d9779_row2_col21, #T_d9779_row4_col8, #T_d9779_row6_col20, #T_d9779_row8_col4, #T_d9779_row11_col2, #T_d9779_row19_col1, #T_d9779_row20_col6, #T_d9779_row21_col2 {\n",
       "  background-color: #eda8d1;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_d9779_row1_col20, #T_d9779_row8_col9, #T_d9779_row8_col12, #T_d9779_row9_col8, #T_d9779_row12_col8, #T_d9779_row20_col1 {\n",
       "  background-color: #d65a9f;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_d9779_row1_col21, #T_d9779_row21_col1 {\n",
       "  background-color: #ea9fca;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_d9779_row1_col22, #T_d9779_row4_col22, #T_d9779_row22_col1, #T_d9779_row22_col4 {\n",
       "  background-color: #498d20;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_d9779_row1_col23, #T_d9779_row12_col23, #T_d9779_row22_col23, #T_d9779_row23_col1, #T_d9779_row23_col12, #T_d9779_row23_col22 {\n",
       "  background-color: #6dad36;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_d9779_row2_col3, #T_d9779_row3_col2, #T_d9779_row3_col10, #T_d9779_row4_col12, #T_d9779_row10_col3, #T_d9779_row12_col4 {\n",
       "  background-color: #306f1b;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_d9779_row2_col4, #T_d9779_row4_col2 {\n",
       "  background-color: #286619;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_d9779_row2_col5, #T_d9779_row2_col16, #T_d9779_row5_col2, #T_d9779_row16_col2 {\n",
       "  background-color: #a3d36c;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_d9779_row2_col6, #T_d9779_row6_col2 {\n",
       "  background-color: #a5d56f;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_d9779_row2_col8, #T_d9779_row8_col2 {\n",
       "  background-color: #e07eb3;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_d9779_row2_col9, #T_d9779_row2_col10, #T_d9779_row3_col13, #T_d9779_row3_col14, #T_d9779_row6_col17, #T_d9779_row9_col2, #T_d9779_row10_col2, #T_d9779_row13_col3, #T_d9779_row14_col3, #T_d9779_row17_col6 {\n",
       "  background-color: #397a1d;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_d9779_row2_col13, #T_d9779_row13_col2 {\n",
       "  background-color: #2d6b1a;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_d9779_row2_col14, #T_d9779_row10_col22, #T_d9779_row12_col22, #T_d9779_row14_col2, #T_d9779_row22_col10, #T_d9779_row22_col12 {\n",
       "  background-color: #5a9d29;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_d9779_row2_col15, #T_d9779_row15_col2 {\n",
       "  background-color: #e6f5d0;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_d9779_row2_col17, #T_d9779_row10_col15, #T_d9779_row15_col10, #T_d9779_row17_col2 {\n",
       "  background-color: #d9f0bc;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_d9779_row2_col18, #T_d9779_row9_col23, #T_d9779_row18_col2, #T_d9779_row23_col9 {\n",
       "  background-color: #86c049;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_d9779_row2_col19, #T_d9779_row19_col2 {\n",
       "  background-color: #de77ae;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_d9779_row2_col20, #T_d9779_row11_col16, #T_d9779_row16_col11, #T_d9779_row20_col2 {\n",
       "  background-color: #ca2f88;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_d9779_row2_col22, #T_d9779_row8_col20, #T_d9779_row20_col8, #T_d9779_row22_col2 {\n",
       "  background-color: #4b8f21;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_d9779_row2_col23, #T_d9779_row9_col22, #T_d9779_row22_col9, #T_d9779_row23_col2 {\n",
       "  background-color: #62a32e;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_d9779_row3_col5, #T_d9779_row5_col3, #T_d9779_row13_col14, #T_d9779_row14_col13 {\n",
       "  background-color: #77b53c;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_d9779_row3_col6, #T_d9779_row6_col3, #T_d9779_row9_col14, #T_d9779_row10_col23, #T_d9779_row14_col9, #T_d9779_row23_col10 {\n",
       "  background-color: #7bb93e;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_d9779_row3_col8, #T_d9779_row8_col3 {\n",
       "  background-color: #eeadd4;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_d9779_row3_col11, #T_d9779_row11_col3 {\n",
       "  background-color: #f7cce5;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_d9779_row3_col16, #T_d9779_row16_col3 {\n",
       "  background-color: #c4e699;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_d9779_row3_col17, #T_d9779_row17_col3 {\n",
       "  background-color: #b9e187;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_d9779_row3_col18, #T_d9779_row10_col17, #T_d9779_row17_col10, #T_d9779_row18_col3 {\n",
       "  background-color: #bee490;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_d9779_row3_col19, #T_d9779_row19_col3 {\n",
       "  background-color: #eeabd2;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_d9779_row3_col20, #T_d9779_row20_col3 {\n",
       "  background-color: #cc368b;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_d9779_row3_col21, #T_d9779_row21_col3 {\n",
       "  background-color: #e48bbc;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_d9779_row3_col22, #T_d9779_row22_col3 {\n",
       "  background-color: #4e9322;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_d9779_row3_col23, #T_d9779_row23_col3 {\n",
       "  background-color: #83bf46;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_d9779_row4_col5, #T_d9779_row5_col4, #T_d9779_row5_col9, #T_d9779_row5_col13, #T_d9779_row9_col5, #T_d9779_row13_col5 {\n",
       "  background-color: #9ccf64;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_d9779_row4_col6, #T_d9779_row6_col4 {\n",
       "  background-color: #98cc5f;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_d9779_row4_col9, #T_d9779_row5_col17, #T_d9779_row9_col4, #T_d9779_row10_col12, #T_d9779_row12_col10, #T_d9779_row17_col5 {\n",
       "  background-color: #40831e;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_d9779_row4_col11, #T_d9779_row11_col4, #T_d9779_row15_col16, #T_d9779_row16_col15 {\n",
       "  background-color: #f5c4e1;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_d9779_row4_col14, #T_d9779_row8_col19, #T_d9779_row14_col4, #T_d9779_row19_col8 {\n",
       "  background-color: #468a20;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_d9779_row4_col16, #T_d9779_row5_col22, #T_d9779_row14_col23, #T_d9779_row16_col4, #T_d9779_row22_col5, #T_d9779_row23_col14 {\n",
       "  background-color: #bde38d;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_d9779_row4_col18, #T_d9779_row6_col12, #T_d9779_row12_col6, #T_d9779_row18_col4 {\n",
       "  background-color: #a9d874;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_d9779_row4_col19, #T_d9779_row19_col4, #T_d9779_row20_col23, #T_d9779_row23_col20 {\n",
       "  background-color: #e897c4;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_d9779_row4_col20, #T_d9779_row20_col4 {\n",
       "  background-color: #d24c97;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_d9779_row4_col21, #T_d9779_row9_col11, #T_d9779_row11_col9, #T_d9779_row13_col21, #T_d9779_row14_col20, #T_d9779_row20_col14, #T_d9779_row21_col4, #T_d9779_row21_col13 {\n",
       "  background-color: #eba1cb;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_d9779_row4_col23, #T_d9779_row23_col4 {\n",
       "  background-color: #67a832;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_d9779_row5_col6, #T_d9779_row6_col5 {\n",
       "  background-color: #488c20;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_d9779_row5_col8, #T_d9779_row5_col21, #T_d9779_row8_col5, #T_d9779_row19_col22, #T_d9779_row21_col5, #T_d9779_row22_col19 {\n",
       "  background-color: #f4c1df;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_d9779_row5_col10, #T_d9779_row10_col5 {\n",
       "  background-color: #7fbc41;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_d9779_row5_col11, #T_d9779_row11_col5 {\n",
       "  background-color: #f8d0e7;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_d9779_row5_col12, #T_d9779_row12_col5 {\n",
       "  background-color: #93c959;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_d9779_row5_col14, #T_d9779_row13_col16, #T_d9779_row14_col5, #T_d9779_row16_col13 {\n",
       "  background-color: #75b43b;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_d9779_row5_col15, #T_d9779_row15_col5 {\n",
       "  background-color: #fce5f1;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_d9779_row5_col16, #T_d9779_row15_col20, #T_d9779_row16_col5, #T_d9779_row20_col15 {\n",
       "  background-color: #dff2c4;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_d9779_row5_col18, #T_d9779_row18_col5 {\n",
       "  background-color: #eef6e2;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_d9779_row5_col19, #T_d9779_row19_col5 {\n",
       "  background-color: #fcdded;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_d9779_row5_col20, #T_d9779_row9_col19, #T_d9779_row19_col9, #T_d9779_row20_col5 {\n",
       "  background-color: #d5579d;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_d9779_row5_col23, #T_d9779_row23_col5 {\n",
       "  background-color: #ebf6dc;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_d9779_row6_col8, #T_d9779_row8_col6 {\n",
       "  background-color: #fde2f0;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_d9779_row6_col10, #T_d9779_row10_col6, #T_d9779_row12_col16, #T_d9779_row16_col12 {\n",
       "  background-color: #7dba40;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_d9779_row6_col11, #T_d9779_row11_col6 {\n",
       "  background-color: #faecf3;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_d9779_row6_col13, #T_d9779_row13_col6, #T_d9779_row16_col23, #T_d9779_row23_col16 {\n",
       "  background-color: #b5df82;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_d9779_row6_col14, #T_d9779_row9_col10, #T_d9779_row10_col9, #T_d9779_row14_col6, #T_d9779_row14_col22, #T_d9779_row22_col14 {\n",
       "  background-color: #66a731;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_d9779_row6_col15, #T_d9779_row15_col6 {\n",
       "  background-color: #edf6e1;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_d9779_row6_col16, #T_d9779_row16_col6 {\n",
       "  background-color: #f7f7f7;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_d9779_row6_col18, #T_d9779_row18_col6 {\n",
       "  background-color: #f9eff4;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_d9779_row6_col19, #T_d9779_row19_col6 {\n",
       "  background-color: #f7f6f7;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_d9779_row6_col21, #T_d9779_row9_col21, #T_d9779_row21_col6, #T_d9779_row21_col9 {\n",
       "  background-color: #efb0d6;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_d9779_row6_col22, #T_d9779_row22_col6 {\n",
       "  background-color: #c2e596;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_d9779_row6_col23, #T_d9779_row12_col15, #T_d9779_row13_col15, #T_d9779_row15_col12, #T_d9779_row15_col13, #T_d9779_row23_col6 {\n",
       "  background-color: #f2f6ec;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_d9779_row8_col10, #T_d9779_row8_col22, #T_d9779_row10_col8, #T_d9779_row22_col8 {\n",
       "  background-color: #f1b5d9;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_d9779_row8_col13, #T_d9779_row13_col8 {\n",
       "  background-color: #d14895;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_d9779_row8_col16, #T_d9779_row16_col8, #T_d9779_row18_col20, #T_d9779_row20_col18 {\n",
       "  background-color: #c51d7e;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_d9779_row8_col17, #T_d9779_row17_col8 {\n",
       "  background-color: #fbd8eb;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_d9779_row8_col18, #T_d9779_row18_col8 {\n",
       "  background-color: #ac0f6a;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_d9779_row8_col21, #T_d9779_row21_col8 {\n",
       "  background-color: #eaf5d9;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_d9779_row8_col23, #T_d9779_row23_col8 {\n",
       "  background-color: #f1b7da;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_d9779_row9_col15, #T_d9779_row14_col19, #T_d9779_row15_col9, #T_d9779_row19_col14 {\n",
       "  background-color: #f8f5f6;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_d9779_row9_col16, #T_d9779_row16_col9 {\n",
       "  background-color: #8cc551;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_d9779_row9_col18, #T_d9779_row18_col9 {\n",
       "  background-color: #6bac34;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_d9779_row9_col20, #T_d9779_row11_col18, #T_d9779_row18_col11, #T_d9779_row20_col9 {\n",
       "  background-color: #bd1777;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_d9779_row10_col11, #T_d9779_row11_col10 {\n",
       "  background-color: #f6c7e3;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_d9779_row10_col13, #T_d9779_row13_col10 {\n",
       "  background-color: #45881f;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_d9779_row10_col14, #T_d9779_row14_col10 {\n",
       "  background-color: #3f811e;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_d9779_row10_col20, #T_d9779_row20_col10 {\n",
       "  background-color: #d4539b;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_d9779_row10_col21, #T_d9779_row19_col23, #T_d9779_row21_col10, #T_d9779_row23_col19 {\n",
       "  background-color: #e590bf;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_d9779_row11_col12, #T_d9779_row12_col11, #T_d9779_row20_col22, #T_d9779_row22_col20 {\n",
       "  background-color: #e388ba;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_d9779_row11_col13, #T_d9779_row13_col11 {\n",
       "  background-color: #df79b0;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_d9779_row11_col14, #T_d9779_row14_col11 {\n",
       "  background-color: #f4f7f0;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_d9779_row11_col15, #T_d9779_row14_col15, #T_d9779_row15_col11, #T_d9779_row15_col14, #T_d9779_row15_col22, #T_d9779_row22_col15 {\n",
       "  background-color: #d8efb9;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_d9779_row11_col17, #T_d9779_row17_col11 {\n",
       "  background-color: #fde1ef;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_d9779_row11_col19, #T_d9779_row19_col11, #T_d9779_row19_col20, #T_d9779_row20_col19 {\n",
       "  background-color: #4c9121;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_d9779_row11_col20, #T_d9779_row20_col11 {\n",
       "  background-color: #549825;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_d9779_row11_col21, #T_d9779_row21_col11 {\n",
       "  background-color: #f3f6ed;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_d9779_row11_col22, #T_d9779_row22_col11 {\n",
       "  background-color: #f8cee6;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_d9779_row11_col23, #T_d9779_row23_col11 {\n",
       "  background-color: #eca6cf;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_d9779_row12_col14, #T_d9779_row12_col18, #T_d9779_row13_col23, #T_d9779_row14_col12, #T_d9779_row18_col12, #T_d9779_row23_col13 {\n",
       "  background-color: #69aa33;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_d9779_row12_col17, #T_d9779_row17_col12 {\n",
       "  background-color: #d0ecad;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_d9779_row12_col19, #T_d9779_row19_col12 {\n",
       "  background-color: #ce3d8f;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_d9779_row12_col20, #T_d9779_row20_col12 {\n",
       "  background-color: #b1116d;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_d9779_row12_col21, #T_d9779_row21_col12 {\n",
       "  background-color: #e89ac6;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_d9779_row13_col18, #T_d9779_row13_col22, #T_d9779_row18_col13, #T_d9779_row22_col13 {\n",
       "  background-color: #5ea02c;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_d9779_row13_col19, #T_d9779_row19_col13 {\n",
       "  background-color: #c82884;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_d9779_row13_col20, #T_d9779_row20_col13 {\n",
       "  background-color: #ae106b;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_d9779_row14_col16, #T_d9779_row16_col14 {\n",
       "  background-color: #f8f3f6;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_d9779_row14_col17, #T_d9779_row17_col14 {\n",
       "  background-color: #acd977;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_d9779_row14_col18, #T_d9779_row18_col14 {\n",
       "  background-color: #f9f1f5;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_d9779_row14_col21, #T_d9779_row21_col14 {\n",
       "  background-color: #e692c1;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_d9779_row15_col17, #T_d9779_row17_col15 {\n",
       "  background-color: #faeaf2;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_d9779_row15_col18, #T_d9779_row18_col15 {\n",
       "  background-color: #fddeee;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_d9779_row15_col21, #T_d9779_row21_col15, #T_d9779_row21_col23, #T_d9779_row23_col21 {\n",
       "  background-color: #f7f7f6;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_d9779_row15_col23, #T_d9779_row23_col15 {\n",
       "  background-color: #ebf6db;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_d9779_row16_col19, #T_d9779_row18_col19, #T_d9779_row19_col16, #T_d9779_row19_col18 {\n",
       "  background-color: #8e0152;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_d9779_row16_col20, #T_d9779_row20_col16 {\n",
       "  background-color: #c01879;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_d9779_row16_col21, #T_d9779_row21_col16 {\n",
       "  background-color: #fbd9ec;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_d9779_row17_col18, #T_d9779_row18_col17 {\n",
       "  background-color: #f8f4f6;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_d9779_row17_col20, #T_d9779_row20_col17 {\n",
       "  background-color: #eba3cd;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_d9779_row17_col21, #T_d9779_row21_col17 {\n",
       "  background-color: #f9d3e8;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_d9779_row17_col22, #T_d9779_row22_col17 {\n",
       "  background-color: #e9f5d6;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_d9779_row17_col23, #T_d9779_row21_col22, #T_d9779_row22_col21, #T_d9779_row23_col17 {\n",
       "  background-color: #f9f0f5;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_d9779_row18_col21, #T_d9779_row21_col18 {\n",
       "  background-color: #fcdbed;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_d9779_row18_col23, #T_d9779_row23_col18 {\n",
       "  background-color: #a1d26a;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_d9779_row20_col21, #T_d9779_row21_col20 {\n",
       "  background-color: #c6e79c;\n",
       "  color: #000000;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_d9779\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_d9779_level0_col0\" class=\"col_heading level0 col0\" >label</th>\n",
       "      <th id=\"T_d9779_level0_col1\" class=\"col_heading level0 col1\" >area</th>\n",
       "      <th id=\"T_d9779_level0_col2\" class=\"col_heading level0 col2\" >bbox_area</th>\n",
       "      <th id=\"T_d9779_level0_col3\" class=\"col_heading level0 col3\" >equivalent_diameter</th>\n",
       "      <th id=\"T_d9779_level0_col4\" class=\"col_heading level0 col4\" >convex_area</th>\n",
       "      <th id=\"T_d9779_level0_col5\" class=\"col_heading level0 col5\" >max_intensity</th>\n",
       "      <th id=\"T_d9779_level0_col6\" class=\"col_heading level0 col6\" >mean_intensity</th>\n",
       "      <th id=\"T_d9779_level0_col7\" class=\"col_heading level0 col7\" >min_intensity</th>\n",
       "      <th id=\"T_d9779_level0_col8\" class=\"col_heading level0 col8\" >extent</th>\n",
       "      <th id=\"T_d9779_level0_col9\" class=\"col_heading level0 col9\" >local_centroid-0</th>\n",
       "      <th id=\"T_d9779_level0_col10\" class=\"col_heading level0 col10\" >local_centroid-1</th>\n",
       "      <th id=\"T_d9779_level0_col11\" class=\"col_heading level0 col11\" >solidity</th>\n",
       "      <th id=\"T_d9779_level0_col12\" class=\"col_heading level0 col12\" >feret_diameter_max</th>\n",
       "      <th id=\"T_d9779_level0_col13\" class=\"col_heading level0 col13\" >major_axis_length</th>\n",
       "      <th id=\"T_d9779_level0_col14\" class=\"col_heading level0 col14\" >minor_axis_length</th>\n",
       "      <th id=\"T_d9779_level0_col15\" class=\"col_heading level0 col15\" >orientation</th>\n",
       "      <th id=\"T_d9779_level0_col16\" class=\"col_heading level0 col16\" >eccentricity</th>\n",
       "      <th id=\"T_d9779_level0_col17\" class=\"col_heading level0 col17\" >standard_deviation_intensity</th>\n",
       "      <th id=\"T_d9779_level0_col18\" class=\"col_heading level0 col18\" >aspect_ratio</th>\n",
       "      <th id=\"T_d9779_level0_col19\" class=\"col_heading level0 col19\" >roundness</th>\n",
       "      <th id=\"T_d9779_level0_col20\" class=\"col_heading level0 col20\" >circularity</th>\n",
       "      <th id=\"T_d9779_level0_col21\" class=\"col_heading level0 col21\" >UMAP0</th>\n",
       "      <th id=\"T_d9779_level0_col22\" class=\"col_heading level0 col22\" >UMAP1</th>\n",
       "      <th id=\"T_d9779_level0_col23\" class=\"col_heading level0 col23\" >MANUAL_CLUSTER_ID</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_d9779_level0_row0\" class=\"row_heading level0 row0\" >label</th>\n",
       "      <td id=\"T_d9779_row0_col0\" class=\"data row0 col0\" >1.000000</td>\n",
       "      <td id=\"T_d9779_row0_col1\" class=\"data row0 col1\" >0.261682</td>\n",
       "      <td id=\"T_d9779_row0_col2\" class=\"data row0 col2\" >0.223070</td>\n",
       "      <td id=\"T_d9779_row0_col3\" class=\"data row0 col3\" >0.249249</td>\n",
       "      <td id=\"T_d9779_row0_col4\" class=\"data row0 col4\" >0.250594</td>\n",
       "      <td id=\"T_d9779_row0_col5\" class=\"data row0 col5\" >0.110791</td>\n",
       "      <td id=\"T_d9779_row0_col6\" class=\"data row0 col6\" >0.235692</td>\n",
       "      <td id=\"T_d9779_row0_col7\" class=\"data row0 col7\" >nan</td>\n",
       "      <td id=\"T_d9779_row0_col8\" class=\"data row0 col8\" >0.031673</td>\n",
       "      <td id=\"T_d9779_row0_col9\" class=\"data row0 col9\" >0.177363</td>\n",
       "      <td id=\"T_d9779_row0_col10\" class=\"data row0 col10\" >0.227746</td>\n",
       "      <td id=\"T_d9779_row0_col11\" class=\"data row0 col11\" >0.090163</td>\n",
       "      <td id=\"T_d9779_row0_col12\" class=\"data row0 col12\" >0.208067</td>\n",
       "      <td id=\"T_d9779_row0_col13\" class=\"data row0 col13\" >0.198908</td>\n",
       "      <td id=\"T_d9779_row0_col14\" class=\"data row0 col14\" >0.237521</td>\n",
       "      <td id=\"T_d9779_row0_col15\" class=\"data row0 col15\" >0.319053</td>\n",
       "      <td id=\"T_d9779_row0_col16\" class=\"data row0 col16\" >0.059804</td>\n",
       "      <td id=\"T_d9779_row0_col17\" class=\"data row0 col17\" >0.189165</td>\n",
       "      <td id=\"T_d9779_row0_col18\" class=\"data row0 col18\" >0.036433</td>\n",
       "      <td id=\"T_d9779_row0_col19\" class=\"data row0 col19\" >-0.055815</td>\n",
       "      <td id=\"T_d9779_row0_col20\" class=\"data row0 col20\" >-0.054152</td>\n",
       "      <td id=\"T_d9779_row0_col21\" class=\"data row0 col21\" >-0.065835</td>\n",
       "      <td id=\"T_d9779_row0_col22\" class=\"data row0 col22\" >0.139702</td>\n",
       "      <td id=\"T_d9779_row0_col23\" class=\"data row0 col23\" >0.080739</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_d9779_level0_row1\" class=\"row_heading level0 row1\" >area</th>\n",
       "      <td id=\"T_d9779_row1_col0\" class=\"data row1 col0\" >0.261682</td>\n",
       "      <td id=\"T_d9779_row1_col1\" class=\"data row1 col1\" >1.000000</td>\n",
       "      <td id=\"T_d9779_row1_col2\" class=\"data row1 col2\" >0.973718</td>\n",
       "      <td id=\"T_d9779_row1_col3\" class=\"data row1 col3\" >0.978723</td>\n",
       "      <td id=\"T_d9779_row1_col4\" class=\"data row1 col4\" >0.997560</td>\n",
       "      <td id=\"T_d9779_row1_col5\" class=\"data row1 col5\" >0.511730</td>\n",
       "      <td id=\"T_d9779_row1_col6\" class=\"data row1 col6\" >0.530250</td>\n",
       "      <td id=\"T_d9779_row1_col7\" class=\"data row1 col7\" >nan</td>\n",
       "      <td id=\"T_d9779_row1_col8\" class=\"data row1 col8\" >-0.362472</td>\n",
       "      <td id=\"T_d9779_row1_col9\" class=\"data row1 col9\" >0.847281</td>\n",
       "      <td id=\"T_d9779_row1_col10\" class=\"data row1 col10\" >0.935689</td>\n",
       "      <td id=\"T_d9779_row1_col11\" class=\"data row1 col11\" >-0.243908</td>\n",
       "      <td id=\"T_d9779_row1_col12\" class=\"data row1 col12\" >0.930981</td>\n",
       "      <td id=\"T_d9779_row1_col13\" class=\"data row1 col13\" >0.911069</td>\n",
       "      <td id=\"T_d9779_row1_col14\" class=\"data row1 col14\" >0.859240</td>\n",
       "      <td id=\"T_d9779_row1_col15\" class=\"data row1 col15\" >0.280673</td>\n",
       "      <td id=\"T_d9779_row1_col16\" class=\"data row1 col16\" >0.348585</td>\n",
       "      <td id=\"T_d9779_row1_col17\" class=\"data row1 col17\" >0.288670</td>\n",
       "      <td id=\"T_d9779_row1_col18\" class=\"data row1 col18\" >0.411794</td>\n",
       "      <td id=\"T_d9779_row1_col19\" class=\"data row1 col19\" >-0.415592</td>\n",
       "      <td id=\"T_d9779_row1_col20\" class=\"data row1 col20\" >-0.626241</td>\n",
       "      <td id=\"T_d9779_row1_col21\" class=\"data row1 col21\" >-0.442711</td>\n",
       "      <td id=\"T_d9779_row1_col22\" class=\"data row1 col22\" >0.819263</td>\n",
       "      <td id=\"T_d9779_row1_col23\" class=\"data row1 col23\" >0.677335</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_d9779_level0_row2\" class=\"row_heading level0 row2\" >bbox_area</th>\n",
       "      <td id=\"T_d9779_row2_col0\" class=\"data row2 col0\" >0.223070</td>\n",
       "      <td id=\"T_d9779_row2_col1\" class=\"data row2 col1\" >0.973718</td>\n",
       "      <td id=\"T_d9779_row2_col2\" class=\"data row2 col2\" >1.000000</td>\n",
       "      <td id=\"T_d9779_row2_col3\" class=\"data row2 col3\" >0.948328</td>\n",
       "      <td id=\"T_d9779_row2_col4\" class=\"data row2 col4\" >0.985584</td>\n",
       "      <td id=\"T_d9779_row2_col5\" class=\"data row2 col5\" >0.481524</td>\n",
       "      <td id=\"T_d9779_row2_col6\" class=\"data row2 col6\" >0.476951</td>\n",
       "      <td id=\"T_d9779_row2_col7\" class=\"data row2 col7\" >nan</td>\n",
       "      <td id=\"T_d9779_row2_col8\" class=\"data row2 col8\" >-0.546728</td>\n",
       "      <td id=\"T_d9779_row2_col9\" class=\"data row2 col9\" >0.902854</td>\n",
       "      <td id=\"T_d9779_row2_col10\" class=\"data row2 col10\" >0.904551</td>\n",
       "      <td id=\"T_d9779_row2_col11\" class=\"data row2 col11\" >-0.416707</td>\n",
       "      <td id=\"T_d9779_row2_col12\" class=\"data row2 col12\" >0.973189</td>\n",
       "      <td id=\"T_d9779_row2_col13\" class=\"data row2 col13\" >0.967337</td>\n",
       "      <td id=\"T_d9779_row2_col14\" class=\"data row2 col14\" >0.752580</td>\n",
       "      <td id=\"T_d9779_row2_col15\" class=\"data row2 col15\" >0.213080</td>\n",
       "      <td id=\"T_d9779_row2_col16\" class=\"data row2 col16\" >0.479196</td>\n",
       "      <td id=\"T_d9779_row2_col17\" class=\"data row2 col17\" >0.267528</td>\n",
       "      <td id=\"T_d9779_row2_col18\" class=\"data row2 col18\" >0.581132</td>\n",
       "      <td id=\"T_d9779_row2_col19\" class=\"data row2 col19\" >-0.569335</td>\n",
       "      <td id=\"T_d9779_row2_col20\" class=\"data row2 col20\" >-0.718764</td>\n",
       "      <td id=\"T_d9779_row2_col21\" class=\"data row2 col21\" >-0.413779</td>\n",
       "      <td id=\"T_d9779_row2_col22\" class=\"data row2 col22\" >0.813951</td>\n",
       "      <td id=\"T_d9779_row2_col23\" class=\"data row2 col23\" >0.719434</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_d9779_level0_row3\" class=\"row_heading level0 row3\" >equivalent_diameter</th>\n",
       "      <td id=\"T_d9779_row3_col0\" class=\"data row3 col0\" >0.249249</td>\n",
       "      <td id=\"T_d9779_row3_col1\" class=\"data row3 col1\" >0.978723</td>\n",
       "      <td id=\"T_d9779_row3_col2\" class=\"data row3 col2\" >0.948328</td>\n",
       "      <td id=\"T_d9779_row3_col3\" class=\"data row3 col3\" >1.000000</td>\n",
       "      <td id=\"T_d9779_row3_col4\" class=\"data row3 col4\" >0.974614</td>\n",
       "      <td id=\"T_d9779_row3_col5\" class=\"data row3 col5\" >0.633984</td>\n",
       "      <td id=\"T_d9779_row3_col6\" class=\"data row3 col6\" >0.618553</td>\n",
       "      <td id=\"T_d9779_row3_col7\" class=\"data row3 col7\" >nan</td>\n",
       "      <td id=\"T_d9779_row3_col8\" class=\"data row3 col8\" >-0.395696</td>\n",
       "      <td id=\"T_d9779_row3_col9\" class=\"data row3 col9\" >0.858779</td>\n",
       "      <td id=\"T_d9779_row3_col10\" class=\"data row3 col10\" >0.947036</td>\n",
       "      <td id=\"T_d9779_row3_col11\" class=\"data row3 col11\" >-0.266587</td>\n",
       "      <td id=\"T_d9779_row3_col12\" class=\"data row3 col12\" >0.931696</td>\n",
       "      <td id=\"T_d9779_row3_col13\" class=\"data row3 col13\" >0.904412</td>\n",
       "      <td id=\"T_d9779_row3_col14\" class=\"data row3 col14\" >0.904698</td>\n",
       "      <td id=\"T_d9779_row3_col15\" class=\"data row3 col15\" >0.197456</td>\n",
       "      <td id=\"T_d9779_row3_col16\" class=\"data row3 col16\" >0.363799</td>\n",
       "      <td id=\"T_d9779_row3_col17\" class=\"data row3 col17\" >0.402328</td>\n",
       "      <td id=\"T_d9779_row3_col18\" class=\"data row3 col18\" >0.386884</td>\n",
       "      <td id=\"T_d9779_row3_col19\" class=\"data row3 col19\" >-0.406856</td>\n",
       "      <td id=\"T_d9779_row3_col20\" class=\"data row3 col20\" >-0.701230</td>\n",
       "      <td id=\"T_d9779_row3_col21\" class=\"data row3 col21\" >-0.509190</td>\n",
       "      <td id=\"T_d9779_row3_col22\" class=\"data row3 col22\" >0.793707</td>\n",
       "      <td id=\"T_d9779_row3_col23\" class=\"data row3 col23\" >0.590973</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_d9779_level0_row4\" class=\"row_heading level0 row4\" >convex_area</th>\n",
       "      <td id=\"T_d9779_row4_col0\" class=\"data row4 col0\" >0.250594</td>\n",
       "      <td id=\"T_d9779_row4_col1\" class=\"data row4 col1\" >0.997560</td>\n",
       "      <td id=\"T_d9779_row4_col2\" class=\"data row4 col2\" >0.985584</td>\n",
       "      <td id=\"T_d9779_row4_col3\" class=\"data row4 col3\" >0.974614</td>\n",
       "      <td id=\"T_d9779_row4_col4\" class=\"data row4 col4\" >1.000000</td>\n",
       "      <td id=\"T_d9779_row4_col5\" class=\"data row4 col5\" >0.506730</td>\n",
       "      <td id=\"T_d9779_row4_col6\" class=\"data row4 col6\" >0.517356</td>\n",
       "      <td id=\"T_d9779_row4_col7\" class=\"data row4 col7\" >nan</td>\n",
       "      <td id=\"T_d9779_row4_col8\" class=\"data row4 col8\" >-0.413323</td>\n",
       "      <td id=\"T_d9779_row4_col9\" class=\"data row4 col9\" >0.862417</td>\n",
       "      <td id=\"T_d9779_row4_col10\" class=\"data row4 col10\" >0.934090</td>\n",
       "      <td id=\"T_d9779_row4_col11\" class=\"data row4 col11\" >-0.305706</td>\n",
       "      <td id=\"T_d9779_row4_col12\" class=\"data row4 col12\" >0.948048</td>\n",
       "      <td id=\"T_d9779_row4_col13\" class=\"data row4 col13\" >0.932682</td>\n",
       "      <td id=\"T_d9779_row4_col14\" class=\"data row4 col14\" >0.832264</td>\n",
       "      <td id=\"T_d9779_row4_col15\" class=\"data row4 col15\" >0.263176</td>\n",
       "      <td id=\"T_d9779_row4_col16\" class=\"data row4 col16\" >0.389269</td>\n",
       "      <td id=\"T_d9779_row4_col17\" class=\"data row4 col17\" >0.285105</td>\n",
       "      <td id=\"T_d9779_row4_col18\" class=\"data row4 col18\" >0.462720</td>\n",
       "      <td id=\"T_d9779_row4_col19\" class=\"data row4 col19\" >-0.464090</td>\n",
       "      <td id=\"T_d9779_row4_col20\" class=\"data row4 col20\" >-0.659125</td>\n",
       "      <td id=\"T_d9779_row4_col21\" class=\"data row4 col21\" >-0.435101</td>\n",
       "      <td id=\"T_d9779_row4_col22\" class=\"data row4 col22\" >0.821940</td>\n",
       "      <td id=\"T_d9779_row4_col23\" class=\"data row4 col23\" >0.700457</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_d9779_level0_row5\" class=\"row_heading level0 row5\" >max_intensity</th>\n",
       "      <td id=\"T_d9779_row5_col0\" class=\"data row5 col0\" >0.110791</td>\n",
       "      <td id=\"T_d9779_row5_col1\" class=\"data row5 col1\" >0.511730</td>\n",
       "      <td id=\"T_d9779_row5_col2\" class=\"data row5 col2\" >0.481524</td>\n",
       "      <td id=\"T_d9779_row5_col3\" class=\"data row5 col3\" >0.633984</td>\n",
       "      <td id=\"T_d9779_row5_col4\" class=\"data row5 col4\" >0.506730</td>\n",
       "      <td id=\"T_d9779_row5_col5\" class=\"data row5 col5\" >1.000000</td>\n",
       "      <td id=\"T_d9779_row5_col6\" class=\"data row5 col6\" >0.825115</td>\n",
       "      <td id=\"T_d9779_row5_col7\" class=\"data row5 col7\" >nan</td>\n",
       "      <td id=\"T_d9779_row5_col8\" class=\"data row5 col8\" >-0.324093</td>\n",
       "      <td id=\"T_d9779_row5_col9\" class=\"data row5 col9\" >0.504879</td>\n",
       "      <td id=\"T_d9779_row5_col10\" class=\"data row5 col10\" >0.603305</td>\n",
       "      <td id=\"T_d9779_row5_col11\" class=\"data row5 col11\" >-0.253635</td>\n",
       "      <td id=\"T_d9779_row5_col12\" class=\"data row5 col12\" >0.536089</td>\n",
       "      <td id=\"T_d9779_row5_col13\" class=\"data row5 col13\" >0.502524</td>\n",
       "      <td id=\"T_d9779_row5_col14\" class=\"data row5 col14\" >0.645600</td>\n",
       "      <td id=\"T_d9779_row5_col15\" class=\"data row5 col15\" >-0.139025</td>\n",
       "      <td id=\"T_d9779_row5_col16\" class=\"data row5 col16\" >0.246172</td>\n",
       "      <td id=\"T_d9779_row5_col17\" class=\"data row5 col17\" >0.867057</td>\n",
       "      <td id=\"T_d9779_row5_col18\" class=\"data row5 col18\" >0.121313</td>\n",
       "      <td id=\"T_d9779_row5_col19\" class=\"data row5 col19\" >-0.191680</td>\n",
       "      <td id=\"T_d9779_row5_col20\" class=\"data row5 col20\" >-0.636372</td>\n",
       "      <td id=\"T_d9779_row5_col21\" class=\"data row5 col21\" >-0.324496</td>\n",
       "      <td id=\"T_d9779_row5_col22\" class=\"data row5 col22\" >0.391350</td>\n",
       "      <td id=\"T_d9779_row5_col23\" class=\"data row5 col23\" >0.156570</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_d9779_level0_row6\" class=\"row_heading level0 row6\" >mean_intensity</th>\n",
       "      <td id=\"T_d9779_row6_col0\" class=\"data row6 col0\" >0.235692</td>\n",
       "      <td id=\"T_d9779_row6_col1\" class=\"data row6 col1\" >0.530250</td>\n",
       "      <td id=\"T_d9779_row6_col2\" class=\"data row6 col2\" >0.476951</td>\n",
       "      <td id=\"T_d9779_row6_col3\" class=\"data row6 col3\" >0.618553</td>\n",
       "      <td id=\"T_d9779_row6_col4\" class=\"data row6 col4\" >0.517356</td>\n",
       "      <td id=\"T_d9779_row6_col5\" class=\"data row6 col5\" >0.825115</td>\n",
       "      <td id=\"T_d9779_row6_col6\" class=\"data row6 col6\" >1.000000</td>\n",
       "      <td id=\"T_d9779_row6_col7\" class=\"data row6 col7\" >nan</td>\n",
       "      <td id=\"T_d9779_row6_col8\" class=\"data row6 col8\" >-0.160940</td>\n",
       "      <td id=\"T_d9779_row6_col9\" class=\"data row6 col9\" >0.412859</td>\n",
       "      <td id=\"T_d9779_row6_col10\" class=\"data row6 col10\" >0.609264</td>\n",
       "      <td id=\"T_d9779_row6_col11\" class=\"data row6 col11\" >-0.077797</td>\n",
       "      <td id=\"T_d9779_row6_col12\" class=\"data row6 col12\" >0.458515</td>\n",
       "      <td id=\"T_d9779_row6_col13\" class=\"data row6 col13\" >0.422638</td>\n",
       "      <td id=\"T_d9779_row6_col14\" class=\"data row6 col14\" >0.707711</td>\n",
       "      <td id=\"T_d9779_row6_col15\" class=\"data row6 col15\" >0.132754</td>\n",
       "      <td id=\"T_d9779_row6_col16\" class=\"data row6 col16\" >0.017030</td>\n",
       "      <td id=\"T_d9779_row6_col17\" class=\"data row6 col17\" >0.902001</td>\n",
       "      <td id=\"T_d9779_row6_col18\" class=\"data row6 col18\" >-0.044872</td>\n",
       "      <td id=\"T_d9779_row6_col19\" class=\"data row6 col19\" >0.009002</td>\n",
       "      <td id=\"T_d9779_row6_col20\" class=\"data row6 col20\" >-0.411166</td>\n",
       "      <td id=\"T_d9779_row6_col21\" class=\"data row6 col21\" >-0.387465</td>\n",
       "      <td id=\"T_d9779_row6_col22\" class=\"data row6 col22\" >0.365621</td>\n",
       "      <td id=\"T_d9779_row6_col23\" class=\"data row6 col23\" >0.074372</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_d9779_level0_row7\" class=\"row_heading level0 row7\" >min_intensity</th>\n",
       "      <td id=\"T_d9779_row7_col0\" class=\"data row7 col0\" >nan</td>\n",
       "      <td id=\"T_d9779_row7_col1\" class=\"data row7 col1\" >nan</td>\n",
       "      <td id=\"T_d9779_row7_col2\" class=\"data row7 col2\" >nan</td>\n",
       "      <td id=\"T_d9779_row7_col3\" class=\"data row7 col3\" >nan</td>\n",
       "      <td id=\"T_d9779_row7_col4\" class=\"data row7 col4\" >nan</td>\n",
       "      <td id=\"T_d9779_row7_col5\" class=\"data row7 col5\" >nan</td>\n",
       "      <td id=\"T_d9779_row7_col6\" class=\"data row7 col6\" >nan</td>\n",
       "      <td id=\"T_d9779_row7_col7\" class=\"data row7 col7\" >nan</td>\n",
       "      <td id=\"T_d9779_row7_col8\" class=\"data row7 col8\" >nan</td>\n",
       "      <td id=\"T_d9779_row7_col9\" class=\"data row7 col9\" >nan</td>\n",
       "      <td id=\"T_d9779_row7_col10\" class=\"data row7 col10\" >nan</td>\n",
       "      <td id=\"T_d9779_row7_col11\" class=\"data row7 col11\" >nan</td>\n",
       "      <td id=\"T_d9779_row7_col12\" class=\"data row7 col12\" >nan</td>\n",
       "      <td id=\"T_d9779_row7_col13\" class=\"data row7 col13\" >nan</td>\n",
       "      <td id=\"T_d9779_row7_col14\" class=\"data row7 col14\" >nan</td>\n",
       "      <td id=\"T_d9779_row7_col15\" class=\"data row7 col15\" >nan</td>\n",
       "      <td id=\"T_d9779_row7_col16\" class=\"data row7 col16\" >nan</td>\n",
       "      <td id=\"T_d9779_row7_col17\" class=\"data row7 col17\" >nan</td>\n",
       "      <td id=\"T_d9779_row7_col18\" class=\"data row7 col18\" >nan</td>\n",
       "      <td id=\"T_d9779_row7_col19\" class=\"data row7 col19\" >nan</td>\n",
       "      <td id=\"T_d9779_row7_col20\" class=\"data row7 col20\" >nan</td>\n",
       "      <td id=\"T_d9779_row7_col21\" class=\"data row7 col21\" >nan</td>\n",
       "      <td id=\"T_d9779_row7_col22\" class=\"data row7 col22\" >nan</td>\n",
       "      <td id=\"T_d9779_row7_col23\" class=\"data row7 col23\" >nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_d9779_level0_row8\" class=\"row_heading level0 row8\" >extent</th>\n",
       "      <td id=\"T_d9779_row8_col0\" class=\"data row8 col0\" >0.031673</td>\n",
       "      <td id=\"T_d9779_row8_col1\" class=\"data row8 col1\" >-0.362472</td>\n",
       "      <td id=\"T_d9779_row8_col2\" class=\"data row8 col2\" >-0.546728</td>\n",
       "      <td id=\"T_d9779_row8_col3\" class=\"data row8 col3\" >-0.395696</td>\n",
       "      <td id=\"T_d9779_row8_col4\" class=\"data row8 col4\" >-0.413323</td>\n",
       "      <td id=\"T_d9779_row8_col5\" class=\"data row8 col5\" >-0.324093</td>\n",
       "      <td id=\"T_d9779_row8_col6\" class=\"data row8 col6\" >-0.160940</td>\n",
       "      <td id=\"T_d9779_row8_col7\" class=\"data row8 col7\" >nan</td>\n",
       "      <td id=\"T_d9779_row8_col8\" class=\"data row8 col8\" >1.000000</td>\n",
       "      <td id=\"T_d9779_row8_col9\" class=\"data row8 col9\" >-0.631158</td>\n",
       "      <td id=\"T_d9779_row8_col10\" class=\"data row8 col10\" >-0.375580</td>\n",
       "      <td id=\"T_d9779_row8_col11\" class=\"data row8 col11\" >0.853431</td>\n",
       "      <td id=\"T_d9779_row8_col12\" class=\"data row8 col12\" >-0.631776</td>\n",
       "      <td id=\"T_d9779_row8_col13\" class=\"data row8 col13\" >-0.664733</td>\n",
       "      <td id=\"T_d9779_row8_col14\" class=\"data row8 col14\" >-0.062873</td>\n",
       "      <td id=\"T_d9779_row8_col15\" class=\"data row8 col15\" >0.252915</td>\n",
       "      <td id=\"T_d9779_row8_col16\" class=\"data row8 col16\" >-0.756019</td>\n",
       "      <td id=\"T_d9779_row8_col17\" class=\"data row8 col17\" >-0.216260</td>\n",
       "      <td id=\"T_d9779_row8_col18\" class=\"data row8 col18\" >-0.848271</td>\n",
       "      <td id=\"T_d9779_row8_col19\" class=\"data row8 col19\" >0.834550</td>\n",
       "      <td id=\"T_d9779_row8_col20\" class=\"data row8 col20\" >0.808533</td>\n",
       "      <td id=\"T_d9779_row8_col21\" class=\"data row8 col21\" >0.168523</td>\n",
       "      <td id=\"T_d9779_row8_col22\" class=\"data row8 col22\" >-0.375632</td>\n",
       "      <td id=\"T_d9779_row8_col23\" class=\"data row8 col23\" >-0.371454</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_d9779_level0_row9\" class=\"row_heading level0 row9\" >local_centroid-0</th>\n",
       "      <td id=\"T_d9779_row9_col0\" class=\"data row9 col0\" >0.177363</td>\n",
       "      <td id=\"T_d9779_row9_col1\" class=\"data row9 col1\" >0.847281</td>\n",
       "      <td id=\"T_d9779_row9_col2\" class=\"data row9 col2\" >0.902854</td>\n",
       "      <td id=\"T_d9779_row9_col3\" class=\"data row9 col3\" >0.858779</td>\n",
       "      <td id=\"T_d9779_row9_col4\" class=\"data row9 col4\" >0.862417</td>\n",
       "      <td id=\"T_d9779_row9_col5\" class=\"data row9 col5\" >0.504879</td>\n",
       "      <td id=\"T_d9779_row9_col6\" class=\"data row9 col6\" >0.412859</td>\n",
       "      <td id=\"T_d9779_row9_col7\" class=\"data row9 col7\" >nan</td>\n",
       "      <td id=\"T_d9779_row9_col8\" class=\"data row9 col8\" >-0.631158</td>\n",
       "      <td id=\"T_d9779_row9_col9\" class=\"data row9 col9\" >1.000000</td>\n",
       "      <td id=\"T_d9779_row9_col10\" class=\"data row9 col10\" >0.706437</td>\n",
       "      <td id=\"T_d9779_row9_col11\" class=\"data row9 col11\" >-0.439244</td>\n",
       "      <td id=\"T_d9779_row9_col12\" class=\"data row9 col12\" >0.937673</td>\n",
       "      <td id=\"T_d9779_row9_col13\" class=\"data row9 col13\" >0.932889</td>\n",
       "      <td id=\"T_d9779_row9_col14\" class=\"data row9 col14\" >0.623186</td>\n",
       "      <td id=\"T_d9779_row9_col15\" class=\"data row9 col15\" >0.003490</td>\n",
       "      <td id=\"T_d9779_row9_col16\" class=\"data row9 col16\" >0.560853</td>\n",
       "      <td id=\"T_d9779_row9_col17\" class=\"data row9 col17\" >0.284331</td>\n",
       "      <td id=\"T_d9779_row9_col18\" class=\"data row9 col18\" >0.678234</td>\n",
       "      <td id=\"T_d9779_row9_col19\" class=\"data row9 col19\" >-0.638667</td>\n",
       "      <td id=\"T_d9779_row9_col20\" class=\"data row9 col20\" >-0.785693</td>\n",
       "      <td id=\"T_d9779_row9_col21\" class=\"data row9 col21\" >-0.391875</td>\n",
       "      <td id=\"T_d9779_row9_col22\" class=\"data row9 col22\" >0.720004</td>\n",
       "      <td id=\"T_d9779_row9_col23\" class=\"data row9 col23\" >0.582543</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_d9779_level0_row10\" class=\"row_heading level0 row10\" >local_centroid-1</th>\n",
       "      <td id=\"T_d9779_row10_col0\" class=\"data row10 col0\" >0.227746</td>\n",
       "      <td id=\"T_d9779_row10_col1\" class=\"data row10 col1\" >0.935689</td>\n",
       "      <td id=\"T_d9779_row10_col2\" class=\"data row10 col2\" >0.904551</td>\n",
       "      <td id=\"T_d9779_row10_col3\" class=\"data row10 col3\" >0.947036</td>\n",
       "      <td id=\"T_d9779_row10_col4\" class=\"data row10 col4\" >0.934090</td>\n",
       "      <td id=\"T_d9779_row10_col5\" class=\"data row10 col5\" >0.603305</td>\n",
       "      <td id=\"T_d9779_row10_col6\" class=\"data row10 col6\" >0.609264</td>\n",
       "      <td id=\"T_d9779_row10_col7\" class=\"data row10 col7\" >nan</td>\n",
       "      <td id=\"T_d9779_row10_col8\" class=\"data row10 col8\" >-0.375580</td>\n",
       "      <td id=\"T_d9779_row10_col9\" class=\"data row10 col9\" >0.706437</td>\n",
       "      <td id=\"T_d9779_row10_col10\" class=\"data row10 col10\" >1.000000</td>\n",
       "      <td id=\"T_d9779_row10_col11\" class=\"data row10 col11\" >-0.290177</td>\n",
       "      <td id=\"T_d9779_row10_col12\" class=\"data row10 col12\" >0.863585</td>\n",
       "      <td id=\"T_d9779_row10_col13\" class=\"data row10 col13\" >0.840724</td>\n",
       "      <td id=\"T_d9779_row10_col14\" class=\"data row10 col14\" >0.875044</td>\n",
       "      <td id=\"T_d9779_row10_col15\" class=\"data row10 col15\" >0.271191</td>\n",
       "      <td id=\"T_d9779_row10_col16\" class=\"data row10 col16\" >0.318154</td>\n",
       "      <td id=\"T_d9779_row10_col17\" class=\"data row10 col17\" >0.379400</td>\n",
       "      <td id=\"T_d9779_row10_col18\" class=\"data row10 col18\" >0.321805</td>\n",
       "      <td id=\"T_d9779_row10_col19\" class=\"data row10 col19\" >-0.359961</td>\n",
       "      <td id=\"T_d9779_row10_col20\" class=\"data row10 col20\" >-0.644979</td>\n",
       "      <td id=\"T_d9779_row10_col21\" class=\"data row10 col21\" >-0.488311</td>\n",
       "      <td id=\"T_d9779_row10_col22\" class=\"data row10 col22\" >0.753502</td>\n",
       "      <td id=\"T_d9779_row10_col23\" class=\"data row10 col23\" >0.616873</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_d9779_level0_row11\" class=\"row_heading level0 row11\" >solidity</th>\n",
       "      <td id=\"T_d9779_row11_col0\" class=\"data row11 col0\" >0.090163</td>\n",
       "      <td id=\"T_d9779_row11_col1\" class=\"data row11 col1\" >-0.243908</td>\n",
       "      <td id=\"T_d9779_row11_col2\" class=\"data row11 col2\" >-0.416707</td>\n",
       "      <td id=\"T_d9779_row11_col3\" class=\"data row11 col3\" >-0.266587</td>\n",
       "      <td id=\"T_d9779_row11_col4\" class=\"data row11 col4\" >-0.305706</td>\n",
       "      <td id=\"T_d9779_row11_col5\" class=\"data row11 col5\" >-0.253635</td>\n",
       "      <td id=\"T_d9779_row11_col6\" class=\"data row11 col6\" >-0.077797</td>\n",
       "      <td id=\"T_d9779_row11_col7\" class=\"data row11 col7\" >nan</td>\n",
       "      <td id=\"T_d9779_row11_col8\" class=\"data row11 col8\" >0.853431</td>\n",
       "      <td id=\"T_d9779_row11_col9\" class=\"data row11 col9\" >-0.439244</td>\n",
       "      <td id=\"T_d9779_row11_col10\" class=\"data row11 col10\" >-0.290177</td>\n",
       "      <td id=\"T_d9779_row11_col11\" class=\"data row11 col11\" >1.000000</td>\n",
       "      <td id=\"T_d9779_row11_col12\" class=\"data row11 col12\" >-0.512903</td>\n",
       "      <td id=\"T_d9779_row11_col13\" class=\"data row11 col13\" >-0.556555</td>\n",
       "      <td id=\"T_d9779_row11_col14\" class=\"data row11 col14\" >0.049965</td>\n",
       "      <td id=\"T_d9779_row11_col15\" class=\"data row11 col15\" >0.279509</td>\n",
       "      <td id=\"T_d9779_row11_col16\" class=\"data row11 col16\" >-0.723572</td>\n",
       "      <td id=\"T_d9779_row11_col17\" class=\"data row11 col17\" >-0.169801</td>\n",
       "      <td id=\"T_d9779_row11_col18\" class=\"data row11 col18\" >-0.787587</td>\n",
       "      <td id=\"T_d9779_row11_col19\" class=\"data row11 col19\" >0.801971</td>\n",
       "      <td id=\"T_d9779_row11_col20\" class=\"data row11 col20\" >0.773934</td>\n",
       "      <td id=\"T_d9779_row11_col21\" class=\"data row11 col21\" >0.068021</td>\n",
       "      <td id=\"T_d9779_row11_col22\" class=\"data row11 col22\" >-0.260000</td>\n",
       "      <td id=\"T_d9779_row11_col23\" class=\"data row11 col23\" >-0.418390</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_d9779_level0_row12\" class=\"row_heading level0 row12\" >feret_diameter_max</th>\n",
       "      <td id=\"T_d9779_row12_col0\" class=\"data row12 col0\" >0.208067</td>\n",
       "      <td id=\"T_d9779_row12_col1\" class=\"data row12 col1\" >0.930981</td>\n",
       "      <td id=\"T_d9779_row12_col2\" class=\"data row12 col2\" >0.973189</td>\n",
       "      <td id=\"T_d9779_row12_col3\" class=\"data row12 col3\" >0.931696</td>\n",
       "      <td id=\"T_d9779_row12_col4\" class=\"data row12 col4\" >0.948048</td>\n",
       "      <td id=\"T_d9779_row12_col5\" class=\"data row12 col5\" >0.536089</td>\n",
       "      <td id=\"T_d9779_row12_col6\" class=\"data row12 col6\" >0.458515</td>\n",
       "      <td id=\"T_d9779_row12_col7\" class=\"data row12 col7\" >nan</td>\n",
       "      <td id=\"T_d9779_row12_col8\" class=\"data row12 col8\" >-0.631776</td>\n",
       "      <td id=\"T_d9779_row12_col9\" class=\"data row12 col9\" >0.937673</td>\n",
       "      <td id=\"T_d9779_row12_col10\" class=\"data row12 col10\" >0.863585</td>\n",
       "      <td id=\"T_d9779_row12_col11\" class=\"data row12 col11\" >-0.512903</td>\n",
       "      <td id=\"T_d9779_row12_col12\" class=\"data row12 col12\" >1.000000</td>\n",
       "      <td id=\"T_d9779_row12_col13\" class=\"data row12 col13\" >0.996744</td>\n",
       "      <td id=\"T_d9779_row12_col14\" class=\"data row12 col14\" >0.690639</td>\n",
       "      <td id=\"T_d9779_row12_col15\" class=\"data row12 col15\" >0.077145</td>\n",
       "      <td id=\"T_d9779_row12_col16\" class=\"data row12 col16\" >0.614849</td>\n",
       "      <td id=\"T_d9779_row12_col17\" class=\"data row12 col17\" >0.306228</td>\n",
       "      <td id=\"T_d9779_row12_col18\" class=\"data row12 col18\" >0.690082</td>\n",
       "      <td id=\"T_d9779_row12_col19\" class=\"data row12 col19\" >-0.690444</td>\n",
       "      <td id=\"T_d9779_row12_col20\" class=\"data row12 col20\" >-0.832660</td>\n",
       "      <td id=\"T_d9779_row12_col21\" class=\"data row12 col21\" >-0.457079</td>\n",
       "      <td id=\"T_d9779_row12_col22\" class=\"data row12 col22\" >0.753713</td>\n",
       "      <td id=\"T_d9779_row12_col23\" class=\"data row12 col23\" >0.671673</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_d9779_level0_row13\" class=\"row_heading level0 row13\" >major_axis_length</th>\n",
       "      <td id=\"T_d9779_row13_col0\" class=\"data row13 col0\" >0.198908</td>\n",
       "      <td id=\"T_d9779_row13_col1\" class=\"data row13 col1\" >0.911069</td>\n",
       "      <td id=\"T_d9779_row13_col2\" class=\"data row13 col2\" >0.967337</td>\n",
       "      <td id=\"T_d9779_row13_col3\" class=\"data row13 col3\" >0.904412</td>\n",
       "      <td id=\"T_d9779_row13_col4\" class=\"data row13 col4\" >0.932682</td>\n",
       "      <td id=\"T_d9779_row13_col5\" class=\"data row13 col5\" >0.502524</td>\n",
       "      <td id=\"T_d9779_row13_col6\" class=\"data row13 col6\" >0.422638</td>\n",
       "      <td id=\"T_d9779_row13_col7\" class=\"data row13 col7\" >nan</td>\n",
       "      <td id=\"T_d9779_row13_col8\" class=\"data row13 col8\" >-0.664733</td>\n",
       "      <td id=\"T_d9779_row13_col9\" class=\"data row13 col9\" >0.932889</td>\n",
       "      <td id=\"T_d9779_row13_col10\" class=\"data row13 col10\" >0.840724</td>\n",
       "      <td id=\"T_d9779_row13_col11\" class=\"data row13 col11\" >-0.556555</td>\n",
       "      <td id=\"T_d9779_row13_col12\" class=\"data row13 col12\" >0.996744</td>\n",
       "      <td id=\"T_d9779_row13_col13\" class=\"data row13 col13\" >1.000000</td>\n",
       "      <td id=\"T_d9779_row13_col14\" class=\"data row13 col14\" >0.639308</td>\n",
       "      <td id=\"T_d9779_row13_col15\" class=\"data row13 col15\" >0.076773</td>\n",
       "      <td id=\"T_d9779_row13_col16\" class=\"data row13 col16\" >0.647021</td>\n",
       "      <td id=\"T_d9779_row13_col17\" class=\"data row13 col17\" >0.280378</td>\n",
       "      <td id=\"T_d9779_row13_col18\" class=\"data row13 col18\" >0.736200</td>\n",
       "      <td id=\"T_d9779_row13_col19\" class=\"data row13 col19\" >-0.732103</td>\n",
       "      <td id=\"T_d9779_row13_col20\" class=\"data row13 col20\" >-0.839196</td>\n",
       "      <td id=\"T_d9779_row13_col21\" class=\"data row13 col21\" >-0.437340</td>\n",
       "      <td id=\"T_d9779_row13_col22\" class=\"data row13 col22\" >0.736954</td>\n",
       "      <td id=\"T_d9779_row13_col23\" class=\"data row13 col23\" >0.686248</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_d9779_level0_row14\" class=\"row_heading level0 row14\" >minor_axis_length</th>\n",
       "      <td id=\"T_d9779_row14_col0\" class=\"data row14 col0\" >0.237521</td>\n",
       "      <td id=\"T_d9779_row14_col1\" class=\"data row14 col1\" >0.859240</td>\n",
       "      <td id=\"T_d9779_row14_col2\" class=\"data row14 col2\" >0.752580</td>\n",
       "      <td id=\"T_d9779_row14_col3\" class=\"data row14 col3\" >0.904698</td>\n",
       "      <td id=\"T_d9779_row14_col4\" class=\"data row14 col4\" >0.832264</td>\n",
       "      <td id=\"T_d9779_row14_col5\" class=\"data row14 col5\" >0.645600</td>\n",
       "      <td id=\"T_d9779_row14_col6\" class=\"data row14 col6\" >0.707711</td>\n",
       "      <td id=\"T_d9779_row14_col7\" class=\"data row14 col7\" >nan</td>\n",
       "      <td id=\"T_d9779_row14_col8\" class=\"data row14 col8\" >-0.062873</td>\n",
       "      <td id=\"T_d9779_row14_col9\" class=\"data row14 col9\" >0.623186</td>\n",
       "      <td id=\"T_d9779_row14_col10\" class=\"data row14 col10\" >0.875044</td>\n",
       "      <td id=\"T_d9779_row14_col11\" class=\"data row14 col11\" >0.049965</td>\n",
       "      <td id=\"T_d9779_row14_col12\" class=\"data row14 col12\" >0.690639</td>\n",
       "      <td id=\"T_d9779_row14_col13\" class=\"data row14 col13\" >0.639308</td>\n",
       "      <td id=\"T_d9779_row14_col14\" class=\"data row14 col14\" >1.000000</td>\n",
       "      <td id=\"T_d9779_row14_col15\" class=\"data row14 col15\" >0.278107</td>\n",
       "      <td id=\"T_d9779_row14_col16\" class=\"data row14 col16\" >-0.012148</td>\n",
       "      <td id=\"T_d9779_row14_col17\" class=\"data row14 col17\" >0.455324</td>\n",
       "      <td id=\"T_d9779_row14_col18\" class=\"data row14 col18\" >-0.030443</td>\n",
       "      <td id=\"T_d9779_row14_col19\" class=\"data row14 col19\" >0.003699</td>\n",
       "      <td id=\"T_d9779_row14_col20\" class=\"data row14 col20\" >-0.435236</td>\n",
       "      <td id=\"T_d9779_row14_col21\" class=\"data row14 col21\" >-0.479807</td>\n",
       "      <td id=\"T_d9779_row14_col22\" class=\"data row14 col22\" >0.702828</td>\n",
       "      <td id=\"T_d9779_row14_col23\" class=\"data row14 col23\" >0.387847</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_d9779_level0_row15\" class=\"row_heading level0 row15\" >orientation</th>\n",
       "      <td id=\"T_d9779_row15_col0\" class=\"data row15 col0\" >0.319053</td>\n",
       "      <td id=\"T_d9779_row15_col1\" class=\"data row15 col1\" >0.280673</td>\n",
       "      <td id=\"T_d9779_row15_col2\" class=\"data row15 col2\" >0.213080</td>\n",
       "      <td id=\"T_d9779_row15_col3\" class=\"data row15 col3\" >0.197456</td>\n",
       "      <td id=\"T_d9779_row15_col4\" class=\"data row15 col4\" >0.263176</td>\n",
       "      <td id=\"T_d9779_row15_col5\" class=\"data row15 col5\" >-0.139025</td>\n",
       "      <td id=\"T_d9779_row15_col6\" class=\"data row15 col6\" >0.132754</td>\n",
       "      <td id=\"T_d9779_row15_col7\" class=\"data row15 col7\" >nan</td>\n",
       "      <td id=\"T_d9779_row15_col8\" class=\"data row15 col8\" >0.252915</td>\n",
       "      <td id=\"T_d9779_row15_col9\" class=\"data row15 col9\" >0.003490</td>\n",
       "      <td id=\"T_d9779_row15_col10\" class=\"data row15 col10\" >0.271191</td>\n",
       "      <td id=\"T_d9779_row15_col11\" class=\"data row15 col11\" >0.279509</td>\n",
       "      <td id=\"T_d9779_row15_col12\" class=\"data row15 col12\" >0.077145</td>\n",
       "      <td id=\"T_d9779_row15_col13\" class=\"data row15 col13\" >0.076773</td>\n",
       "      <td id=\"T_d9779_row15_col14\" class=\"data row15 col14\" >0.278107</td>\n",
       "      <td id=\"T_d9779_row15_col15\" class=\"data row15 col15\" >1.000000</td>\n",
       "      <td id=\"T_d9779_row15_col16\" class=\"data row15 col16\" >-0.305652</td>\n",
       "      <td id=\"T_d9779_row15_col17\" class=\"data row15 col17\" >-0.089349</td>\n",
       "      <td id=\"T_d9779_row15_col18\" class=\"data row15 col18\" >-0.181927</td>\n",
       "      <td id=\"T_d9779_row15_col19\" class=\"data row15 col19\" >0.224205</td>\n",
       "      <td id=\"T_d9779_row15_col20\" class=\"data row15 col20\" >0.242901</td>\n",
       "      <td id=\"T_d9779_row15_col21\" class=\"data row15 col21\" >0.025473</td>\n",
       "      <td id=\"T_d9779_row15_col22\" class=\"data row15 col22\" >0.277117</td>\n",
       "      <td id=\"T_d9779_row15_col23\" class=\"data row15 col23\" >0.163152</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_d9779_level0_row16\" class=\"row_heading level0 row16\" >eccentricity</th>\n",
       "      <td id=\"T_d9779_row16_col0\" class=\"data row16 col0\" >0.059804</td>\n",
       "      <td id=\"T_d9779_row16_col1\" class=\"data row16 col1\" >0.348585</td>\n",
       "      <td id=\"T_d9779_row16_col2\" class=\"data row16 col2\" >0.479196</td>\n",
       "      <td id=\"T_d9779_row16_col3\" class=\"data row16 col3\" >0.363799</td>\n",
       "      <td id=\"T_d9779_row16_col4\" class=\"data row16 col4\" >0.389269</td>\n",
       "      <td id=\"T_d9779_row16_col5\" class=\"data row16 col5\" >0.246172</td>\n",
       "      <td id=\"T_d9779_row16_col6\" class=\"data row16 col6\" >0.017030</td>\n",
       "      <td id=\"T_d9779_row16_col7\" class=\"data row16 col7\" >nan</td>\n",
       "      <td id=\"T_d9779_row16_col8\" class=\"data row16 col8\" >-0.756019</td>\n",
       "      <td id=\"T_d9779_row16_col9\" class=\"data row16 col9\" >0.560853</td>\n",
       "      <td id=\"T_d9779_row16_col10\" class=\"data row16 col10\" >0.318154</td>\n",
       "      <td id=\"T_d9779_row16_col11\" class=\"data row16 col11\" >-0.723572</td>\n",
       "      <td id=\"T_d9779_row16_col12\" class=\"data row16 col12\" >0.614849</td>\n",
       "      <td id=\"T_d9779_row16_col13\" class=\"data row16 col13\" >0.647021</td>\n",
       "      <td id=\"T_d9779_row16_col14\" class=\"data row16 col14\" >-0.012148</td>\n",
       "      <td id=\"T_d9779_row16_col15\" class=\"data row16 col15\" >-0.305652</td>\n",
       "      <td id=\"T_d9779_row16_col16\" class=\"data row16 col16\" >1.000000</td>\n",
       "      <td id=\"T_d9779_row16_col17\" class=\"data row16 col17\" >0.107307</td>\n",
       "      <td id=\"T_d9779_row16_col18\" class=\"data row16 col18\" >0.853302</td>\n",
       "      <td id=\"T_d9779_row16_col19\" class=\"data row16 col19\" >-0.955978</td>\n",
       "      <td id=\"T_d9779_row16_col20\" class=\"data row16 col20\" >-0.779895</td>\n",
       "      <td id=\"T_d9779_row16_col21\" class=\"data row16 col21\" >-0.204662</td>\n",
       "      <td id=\"T_d9779_row16_col22\" class=\"data row16 col22\" >0.251959</td>\n",
       "      <td id=\"T_d9779_row16_col23\" class=\"data row16 col23\" >0.424045</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_d9779_level0_row17\" class=\"row_heading level0 row17\" >standard_deviation_intensity</th>\n",
       "      <td id=\"T_d9779_row17_col0\" class=\"data row17 col0\" >0.189165</td>\n",
       "      <td id=\"T_d9779_row17_col1\" class=\"data row17 col1\" >0.288670</td>\n",
       "      <td id=\"T_d9779_row17_col2\" class=\"data row17 col2\" >0.267528</td>\n",
       "      <td id=\"T_d9779_row17_col3\" class=\"data row17 col3\" >0.402328</td>\n",
       "      <td id=\"T_d9779_row17_col4\" class=\"data row17 col4\" >0.285105</td>\n",
       "      <td id=\"T_d9779_row17_col5\" class=\"data row17 col5\" >0.867057</td>\n",
       "      <td id=\"T_d9779_row17_col6\" class=\"data row17 col6\" >0.902001</td>\n",
       "      <td id=\"T_d9779_row17_col7\" class=\"data row17 col7\" >nan</td>\n",
       "      <td id=\"T_d9779_row17_col8\" class=\"data row17 col8\" >-0.216260</td>\n",
       "      <td id=\"T_d9779_row17_col9\" class=\"data row17 col9\" >0.284331</td>\n",
       "      <td id=\"T_d9779_row17_col10\" class=\"data row17 col10\" >0.379400</td>\n",
       "      <td id=\"T_d9779_row17_col11\" class=\"data row17 col11\" >-0.169801</td>\n",
       "      <td id=\"T_d9779_row17_col12\" class=\"data row17 col12\" >0.306228</td>\n",
       "      <td id=\"T_d9779_row17_col13\" class=\"data row17 col13\" >0.280378</td>\n",
       "      <td id=\"T_d9779_row17_col14\" class=\"data row17 col14\" >0.455324</td>\n",
       "      <td id=\"T_d9779_row17_col15\" class=\"data row17 col15\" >-0.089349</td>\n",
       "      <td id=\"T_d9779_row17_col16\" class=\"data row17 col16\" >0.107307</td>\n",
       "      <td id=\"T_d9779_row17_col17\" class=\"data row17 col17\" >1.000000</td>\n",
       "      <td id=\"T_d9779_row17_col18\" class=\"data row17 col18\" >-0.005215</td>\n",
       "      <td id=\"T_d9779_row17_col19\" class=\"data row17 col19\" >-0.054614</td>\n",
       "      <td id=\"T_d9779_row17_col20\" class=\"data row17 col20\" >-0.425500</td>\n",
       "      <td id=\"T_d9779_row17_col21\" class=\"data row17 col21\" >-0.237603</td>\n",
       "      <td id=\"T_d9779_row17_col22\" class=\"data row17 col22\" >0.183921</td>\n",
       "      <td id=\"T_d9779_row17_col23\" class=\"data row17 col23\" >-0.037206</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_d9779_level0_row18\" class=\"row_heading level0 row18\" >aspect_ratio</th>\n",
       "      <td id=\"T_d9779_row18_col0\" class=\"data row18 col0\" >0.036433</td>\n",
       "      <td id=\"T_d9779_row18_col1\" class=\"data row18 col1\" >0.411794</td>\n",
       "      <td id=\"T_d9779_row18_col2\" class=\"data row18 col2\" >0.581132</td>\n",
       "      <td id=\"T_d9779_row18_col3\" class=\"data row18 col3\" >0.386884</td>\n",
       "      <td id=\"T_d9779_row18_col4\" class=\"data row18 col4\" >0.462720</td>\n",
       "      <td id=\"T_d9779_row18_col5\" class=\"data row18 col5\" >0.121313</td>\n",
       "      <td id=\"T_d9779_row18_col6\" class=\"data row18 col6\" >-0.044872</td>\n",
       "      <td id=\"T_d9779_row18_col7\" class=\"data row18 col7\" >nan</td>\n",
       "      <td id=\"T_d9779_row18_col8\" class=\"data row18 col8\" >-0.848271</td>\n",
       "      <td id=\"T_d9779_row18_col9\" class=\"data row18 col9\" >0.678234</td>\n",
       "      <td id=\"T_d9779_row18_col10\" class=\"data row18 col10\" >0.321805</td>\n",
       "      <td id=\"T_d9779_row18_col11\" class=\"data row18 col11\" >-0.787587</td>\n",
       "      <td id=\"T_d9779_row18_col12\" class=\"data row18 col12\" >0.690082</td>\n",
       "      <td id=\"T_d9779_row18_col13\" class=\"data row18 col13\" >0.736200</td>\n",
       "      <td id=\"T_d9779_row18_col14\" class=\"data row18 col14\" >-0.030443</td>\n",
       "      <td id=\"T_d9779_row18_col15\" class=\"data row18 col15\" >-0.181927</td>\n",
       "      <td id=\"T_d9779_row18_col16\" class=\"data row18 col16\" >0.853302</td>\n",
       "      <td id=\"T_d9779_row18_col17\" class=\"data row18 col17\" >-0.005215</td>\n",
       "      <td id=\"T_d9779_row18_col18\" class=\"data row18 col18\" >1.000000</td>\n",
       "      <td id=\"T_d9779_row18_col19\" class=\"data row18 col19\" >-0.961681</td>\n",
       "      <td id=\"T_d9779_row18_col20\" class=\"data row18 col20\" >-0.756787</td>\n",
       "      <td id=\"T_d9779_row18_col21\" class=\"data row18 col21\" >-0.195762</td>\n",
       "      <td id=\"T_d9779_row18_col22\" class=\"data row18 col22\" >0.321115</td>\n",
       "      <td id=\"T_d9779_row18_col23\" class=\"data row18 col23\" >0.488872</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_d9779_level0_row19\" class=\"row_heading level0 row19\" >roundness</th>\n",
       "      <td id=\"T_d9779_row19_col0\" class=\"data row19 col0\" >-0.055815</td>\n",
       "      <td id=\"T_d9779_row19_col1\" class=\"data row19 col1\" >-0.415592</td>\n",
       "      <td id=\"T_d9779_row19_col2\" class=\"data row19 col2\" >-0.569335</td>\n",
       "      <td id=\"T_d9779_row19_col3\" class=\"data row19 col3\" >-0.406856</td>\n",
       "      <td id=\"T_d9779_row19_col4\" class=\"data row19 col4\" >-0.464090</td>\n",
       "      <td id=\"T_d9779_row19_col5\" class=\"data row19 col5\" >-0.191680</td>\n",
       "      <td id=\"T_d9779_row19_col6\" class=\"data row19 col6\" >0.009002</td>\n",
       "      <td id=\"T_d9779_row19_col7\" class=\"data row19 col7\" >nan</td>\n",
       "      <td id=\"T_d9779_row19_col8\" class=\"data row19 col8\" >0.834550</td>\n",
       "      <td id=\"T_d9779_row19_col9\" class=\"data row19 col9\" >-0.638667</td>\n",
       "      <td id=\"T_d9779_row19_col10\" class=\"data row19 col10\" >-0.359961</td>\n",
       "      <td id=\"T_d9779_row19_col11\" class=\"data row19 col11\" >0.801971</td>\n",
       "      <td id=\"T_d9779_row19_col12\" class=\"data row19 col12\" >-0.690444</td>\n",
       "      <td id=\"T_d9779_row19_col13\" class=\"data row19 col13\" >-0.732103</td>\n",
       "      <td id=\"T_d9779_row19_col14\" class=\"data row19 col14\" >0.003699</td>\n",
       "      <td id=\"T_d9779_row19_col15\" class=\"data row19 col15\" >0.224205</td>\n",
       "      <td id=\"T_d9779_row19_col16\" class=\"data row19 col16\" >-0.955978</td>\n",
       "      <td id=\"T_d9779_row19_col17\" class=\"data row19 col17\" >-0.054614</td>\n",
       "      <td id=\"T_d9779_row19_col18\" class=\"data row19 col18\" >-0.961681</td>\n",
       "      <td id=\"T_d9779_row19_col19\" class=\"data row19 col19\" >1.000000</td>\n",
       "      <td id=\"T_d9779_row19_col20\" class=\"data row19 col20\" >0.802247</td>\n",
       "      <td id=\"T_d9779_row19_col21\" class=\"data row19 col21\" >0.219336</td>\n",
       "      <td id=\"T_d9779_row19_col22\" class=\"data row19 col22\" >-0.318072</td>\n",
       "      <td id=\"T_d9779_row19_col23\" class=\"data row19 col23\" >-0.489533</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_d9779_level0_row20\" class=\"row_heading level0 row20\" >circularity</th>\n",
       "      <td id=\"T_d9779_row20_col0\" class=\"data row20 col0\" >-0.054152</td>\n",
       "      <td id=\"T_d9779_row20_col1\" class=\"data row20 col1\" >-0.626241</td>\n",
       "      <td id=\"T_d9779_row20_col2\" class=\"data row20 col2\" >-0.718764</td>\n",
       "      <td id=\"T_d9779_row20_col3\" class=\"data row20 col3\" >-0.701230</td>\n",
       "      <td id=\"T_d9779_row20_col4\" class=\"data row20 col4\" >-0.659125</td>\n",
       "      <td id=\"T_d9779_row20_col5\" class=\"data row20 col5\" >-0.636372</td>\n",
       "      <td id=\"T_d9779_row20_col6\" class=\"data row20 col6\" >-0.411166</td>\n",
       "      <td id=\"T_d9779_row20_col7\" class=\"data row20 col7\" >nan</td>\n",
       "      <td id=\"T_d9779_row20_col8\" class=\"data row20 col8\" >0.808533</td>\n",
       "      <td id=\"T_d9779_row20_col9\" class=\"data row20 col9\" >-0.785693</td>\n",
       "      <td id=\"T_d9779_row20_col10\" class=\"data row20 col10\" >-0.644979</td>\n",
       "      <td id=\"T_d9779_row20_col11\" class=\"data row20 col11\" >0.773934</td>\n",
       "      <td id=\"T_d9779_row20_col12\" class=\"data row20 col12\" >-0.832660</td>\n",
       "      <td id=\"T_d9779_row20_col13\" class=\"data row20 col13\" >-0.839196</td>\n",
       "      <td id=\"T_d9779_row20_col14\" class=\"data row20 col14\" >-0.435236</td>\n",
       "      <td id=\"T_d9779_row20_col15\" class=\"data row20 col15\" >0.242901</td>\n",
       "      <td id=\"T_d9779_row20_col16\" class=\"data row20 col16\" >-0.779895</td>\n",
       "      <td id=\"T_d9779_row20_col17\" class=\"data row20 col17\" >-0.425500</td>\n",
       "      <td id=\"T_d9779_row20_col18\" class=\"data row20 col18\" >-0.756787</td>\n",
       "      <td id=\"T_d9779_row20_col19\" class=\"data row20 col19\" >0.802247</td>\n",
       "      <td id=\"T_d9779_row20_col20\" class=\"data row20 col20\" >1.000000</td>\n",
       "      <td id=\"T_d9779_row20_col21\" class=\"data row20 col21\" >0.354033</td>\n",
       "      <td id=\"T_d9779_row20_col22\" class=\"data row20 col22\" >-0.512930</td>\n",
       "      <td id=\"T_d9779_row20_col23\" class=\"data row20 col23\" >-0.464936</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_d9779_level0_row21\" class=\"row_heading level0 row21\" >UMAP0</th>\n",
       "      <td id=\"T_d9779_row21_col0\" class=\"data row21 col0\" >-0.065835</td>\n",
       "      <td id=\"T_d9779_row21_col1\" class=\"data row21 col1\" >-0.442711</td>\n",
       "      <td id=\"T_d9779_row21_col2\" class=\"data row21 col2\" >-0.413779</td>\n",
       "      <td id=\"T_d9779_row21_col3\" class=\"data row21 col3\" >-0.509190</td>\n",
       "      <td id=\"T_d9779_row21_col4\" class=\"data row21 col4\" >-0.435101</td>\n",
       "      <td id=\"T_d9779_row21_col5\" class=\"data row21 col5\" >-0.324496</td>\n",
       "      <td id=\"T_d9779_row21_col6\" class=\"data row21 col6\" >-0.387465</td>\n",
       "      <td id=\"T_d9779_row21_col7\" class=\"data row21 col7\" >nan</td>\n",
       "      <td id=\"T_d9779_row21_col8\" class=\"data row21 col8\" >0.168523</td>\n",
       "      <td id=\"T_d9779_row21_col9\" class=\"data row21 col9\" >-0.391875</td>\n",
       "      <td id=\"T_d9779_row21_col10\" class=\"data row21 col10\" >-0.488311</td>\n",
       "      <td id=\"T_d9779_row21_col11\" class=\"data row21 col11\" >0.068021</td>\n",
       "      <td id=\"T_d9779_row21_col12\" class=\"data row21 col12\" >-0.457079</td>\n",
       "      <td id=\"T_d9779_row21_col13\" class=\"data row21 col13\" >-0.437340</td>\n",
       "      <td id=\"T_d9779_row21_col14\" class=\"data row21 col14\" >-0.479807</td>\n",
       "      <td id=\"T_d9779_row21_col15\" class=\"data row21 col15\" >0.025473</td>\n",
       "      <td id=\"T_d9779_row21_col16\" class=\"data row21 col16\" >-0.204662</td>\n",
       "      <td id=\"T_d9779_row21_col17\" class=\"data row21 col17\" >-0.237603</td>\n",
       "      <td id=\"T_d9779_row21_col18\" class=\"data row21 col18\" >-0.195762</td>\n",
       "      <td id=\"T_d9779_row21_col19\" class=\"data row21 col19\" >0.219336</td>\n",
       "      <td id=\"T_d9779_row21_col20\" class=\"data row21 col20\" >0.354033</td>\n",
       "      <td id=\"T_d9779_row21_col21\" class=\"data row21 col21\" >1.000000</td>\n",
       "      <td id=\"T_d9779_row21_col22\" class=\"data row21 col22\" >-0.040889</td>\n",
       "      <td id=\"T_d9779_row21_col23\" class=\"data row21 col23\" >0.023984</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_d9779_level0_row22\" class=\"row_heading level0 row22\" >UMAP1</th>\n",
       "      <td id=\"T_d9779_row22_col0\" class=\"data row22 col0\" >0.139702</td>\n",
       "      <td id=\"T_d9779_row22_col1\" class=\"data row22 col1\" >0.819263</td>\n",
       "      <td id=\"T_d9779_row22_col2\" class=\"data row22 col2\" >0.813951</td>\n",
       "      <td id=\"T_d9779_row22_col3\" class=\"data row22 col3\" >0.793707</td>\n",
       "      <td id=\"T_d9779_row22_col4\" class=\"data row22 col4\" >0.821940</td>\n",
       "      <td id=\"T_d9779_row22_col5\" class=\"data row22 col5\" >0.391350</td>\n",
       "      <td id=\"T_d9779_row22_col6\" class=\"data row22 col6\" >0.365621</td>\n",
       "      <td id=\"T_d9779_row22_col7\" class=\"data row22 col7\" >nan</td>\n",
       "      <td id=\"T_d9779_row22_col8\" class=\"data row22 col8\" >-0.375632</td>\n",
       "      <td id=\"T_d9779_row22_col9\" class=\"data row22 col9\" >0.720004</td>\n",
       "      <td id=\"T_d9779_row22_col10\" class=\"data row22 col10\" >0.753502</td>\n",
       "      <td id=\"T_d9779_row22_col11\" class=\"data row22 col11\" >-0.260000</td>\n",
       "      <td id=\"T_d9779_row22_col12\" class=\"data row22 col12\" >0.753713</td>\n",
       "      <td id=\"T_d9779_row22_col13\" class=\"data row22 col13\" >0.736954</td>\n",
       "      <td id=\"T_d9779_row22_col14\" class=\"data row22 col14\" >0.702828</td>\n",
       "      <td id=\"T_d9779_row22_col15\" class=\"data row22 col15\" >0.277117</td>\n",
       "      <td id=\"T_d9779_row22_col16\" class=\"data row22 col16\" >0.251959</td>\n",
       "      <td id=\"T_d9779_row22_col17\" class=\"data row22 col17\" >0.183921</td>\n",
       "      <td id=\"T_d9779_row22_col18\" class=\"data row22 col18\" >0.321115</td>\n",
       "      <td id=\"T_d9779_row22_col19\" class=\"data row22 col19\" >-0.318072</td>\n",
       "      <td id=\"T_d9779_row22_col20\" class=\"data row22 col20\" >-0.512930</td>\n",
       "      <td id=\"T_d9779_row22_col21\" class=\"data row22 col21\" >-0.040889</td>\n",
       "      <td id=\"T_d9779_row22_col22\" class=\"data row22 col22\" >1.000000</td>\n",
       "      <td id=\"T_d9779_row22_col23\" class=\"data row22 col23\" >0.675625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_d9779_level0_row23\" class=\"row_heading level0 row23\" >MANUAL_CLUSTER_ID</th>\n",
       "      <td id=\"T_d9779_row23_col0\" class=\"data row23 col0\" >0.080739</td>\n",
       "      <td id=\"T_d9779_row23_col1\" class=\"data row23 col1\" >0.677335</td>\n",
       "      <td id=\"T_d9779_row23_col2\" class=\"data row23 col2\" >0.719434</td>\n",
       "      <td id=\"T_d9779_row23_col3\" class=\"data row23 col3\" >0.590973</td>\n",
       "      <td id=\"T_d9779_row23_col4\" class=\"data row23 col4\" >0.700457</td>\n",
       "      <td id=\"T_d9779_row23_col5\" class=\"data row23 col5\" >0.156570</td>\n",
       "      <td id=\"T_d9779_row23_col6\" class=\"data row23 col6\" >0.074372</td>\n",
       "      <td id=\"T_d9779_row23_col7\" class=\"data row23 col7\" >nan</td>\n",
       "      <td id=\"T_d9779_row23_col8\" class=\"data row23 col8\" >-0.371454</td>\n",
       "      <td id=\"T_d9779_row23_col9\" class=\"data row23 col9\" >0.582543</td>\n",
       "      <td id=\"T_d9779_row23_col10\" class=\"data row23 col10\" >0.616873</td>\n",
       "      <td id=\"T_d9779_row23_col11\" class=\"data row23 col11\" >-0.418390</td>\n",
       "      <td id=\"T_d9779_row23_col12\" class=\"data row23 col12\" >0.671673</td>\n",
       "      <td id=\"T_d9779_row23_col13\" class=\"data row23 col13\" >0.686248</td>\n",
       "      <td id=\"T_d9779_row23_col14\" class=\"data row23 col14\" >0.387847</td>\n",
       "      <td id=\"T_d9779_row23_col15\" class=\"data row23 col15\" >0.163152</td>\n",
       "      <td id=\"T_d9779_row23_col16\" class=\"data row23 col16\" >0.424045</td>\n",
       "      <td id=\"T_d9779_row23_col17\" class=\"data row23 col17\" >-0.037206</td>\n",
       "      <td id=\"T_d9779_row23_col18\" class=\"data row23 col18\" >0.488872</td>\n",
       "      <td id=\"T_d9779_row23_col19\" class=\"data row23 col19\" >-0.489533</td>\n",
       "      <td id=\"T_d9779_row23_col20\" class=\"data row23 col20\" >-0.464936</td>\n",
       "      <td id=\"T_d9779_row23_col21\" class=\"data row23 col21\" >0.023984</td>\n",
       "      <td id=\"T_d9779_row23_col22\" class=\"data row23 col22\" >0.675625</td>\n",
       "      <td id=\"T_d9779_row23_col23\" class=\"data row23 col23\" >1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x2d58c20bd60>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def colorize(styler):\n",
    "    styler.background_gradient(axis=None, cmap=\"PiYG\")\n",
    "    return styler\n",
    "\n",
    "df = measurements.corr().T\n",
    "df.style.pipe(colorize)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a91fa985-4202-467e-b9db-6de283960b28",
   "metadata": {},
   "source": [
    "## Exercise\n",
    "The UMAP-generation above is done without parameters such as centroid and orientation. Why?"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "79cb5c6d-a1f8-402c-8328-e0185cdedc57",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Exercise\n",
    "Repeat the procedure above with the dataset human_mitosis. Identify parameters for differentiating the small bright cells from the others. ([hint](https://focalplane.biologists.com/2022/05/23/explorative-image-data-science-with-napari/))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "62d7cb38-117e-4aed-b2e4-929fd1fdd292",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "image = human_mitosis()\n",
    "stackview.insight(image)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "95543960-ae8a-49d1-a1d6-5876c1776f41",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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