notebooks/00-overview.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "0d223f45",
   "metadata": {},
   "source": [
    "# Overview of pylandstats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "40b48313",
   "metadata": {},
   "outputs": [],
   "source": [
    "import swisslandstats as sls\n",
    "\n",
    "import pylandstats as pls"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "53514e90",
   "metadata": {},
   "source": [
    "Running the cell below is required so that:\n",
    "- the land use/land cover (LULC) data is downloaded and preprocessed (see [A03-swisslandstats-preprocessing.ipynb](https://github.com/martibosch/pylandstats-notebooks/blob/main/notebooks/A03-swisslandstats-preprocessing.ipynb) for more details).\n",
    "- the elevation zones vector data is downloaded and preprocessed (see [A04-elev-zones.ipynb](https://github.com/martibosch/pylandstats-notebooks/blob/main/notebooks/A04-elev-zones.ipynb) for more details)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c4239118",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[33mBuilding DAG of jobs...\u001b[0m\r\n",
      "\u001b[33mNothing to be done (all requested files are present and up to date).\u001b[0m\r\n",
      "\u001b[33mComplete log: .snakemake/log/2024-07-25T191825.567508.snakemake.log\u001b[0m\r\n"
     ]
    }
   ],
   "source": [
    "!snakemake -c1 -d .. -s ../Snakefile veveyse_lulc_tifs elev_zones"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4f5c325a",
   "metadata": {},
   "source": [
    "## Landscape analysis\n",
    "\n",
    "We can load landscapes from raster files and compute pandas data frames of patch, class and landscape level. See the notebook [01-landscape-analysis.ipynb](https://github.com/martibosch/pylandstats-notebooks/blob/main/notebooks/01-landscape-analysis.ipynb) for more thorough demonstration."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6759392a",
   "metadata": {},
   "outputs": [],
   "source": [
    "URBAN_CLASS_VAL = 1\n",
    "input_filepath = \"../data/processed/veveyse/LU18_4.tif\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4c9467c8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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PH44jR47g9OnTyMrKSmyPeze0tLRYejdkZ2cjO5tdbSSsSVXNhOiJaAGWqnFARE+kSOlSWVmJffv2oavr7Frlnj17UFRUlBAqiqLg7rvvxm9+8xu8/vrrnnk3EGcxSsWSatRjWorOrmlwJ7yBW2Pp6OjAvn37Ep/379+PcDiM/Px8lJaWoq6uDocOHcK6desSbcLhcGLfY8eOIRwOIysrCyNGjAAA3HnnnVi5ciXmzp2LOXPmYO/evViyZAnuueeexDH88m4gkuERKF7UmxeJXWHiRt0T1uOmYr0UCpQMPtyCZdeuXZg0aVLic9yGMXPmTDQ1NSESiaCtrS1pn/Ly8sT/W1tbsWHDBpSVleHAgQMAgJKSEmzfvh333nsvRo8ejYsuughz587F/PnzE/v55d1AEKmKzNoLubEHG27BMnHiRFNPg6amph7bWDwTxo8fj3fffdfwe7+8GwixyFi10a1cYDxaiBeah1fLe1bBomYkZ7sOjrcdkYwUxntCdrprk8skDFgIavLIoN1nM7orRJpfj5HAW6QE63cjzkKChRCGqBmmV3YDPwz0Rtdm9xpFBkS6JYCDZmsjnEOChUg5eDWVdE05Iss1a3+nhCBSpgo/l2jbTTva8DWOCzkWC+fiAuSh1LPz2YUEC+EYK01FW8hLNvQGWBFaE0tae6fwBlyqhahbGgpLSQA/EB2D1Y42rMJQnMEpocc1oxdycDd2Sy9cpIhjIVIXp0s9dliETYGzqxghOqZGFi3FjCJlxreTEbmDcb/GcU+FCgCcwSkuDWn16tUYPXo0cnNzkZubi/Hjx+O3v/2tiz3shjQWQjqsZrxmnkMs3wVhcHUDv4St2aTCKy9B2YWUWwwYMAD//u//jr//+78HAPzqV7/CD37wA3zwwQe49NJLXTsvCRbCEBE5wWRZBjEjXW0sMqH1HkskwLQZLGklSNIlCLO6ujrp8+LFi7F69Wq8++67rgoWWgojDIkvSaQ6sguVVFnWY0U7GeHVNszaJwuv9NJiOjs7sWnTJpw8eRLjx4939VyksRCOsVMLxYlx3KkWpM22qzdwuyVQeY9rJVSCFlCoveeiHTtYtOx0q1b58ccfY/z48Th16hTOO+88bNmyJZFOyy1IsBBEwEkM1sqmQA6W2oGexass3bQNJwwdOhThcBgnTpzAr3/9a8ycORNvvvmmq8KFBAuhS/catBzLYGYzTO136sFJOxtmiQJPFWRInaOX2kXksl66/JZOycrKShjvr7jiCrz33ntYvnw5nnnmGdfOSYIlbdmc+J/eAEAvbXAIsg2GNa+YGxqK2qU5HQz5cRRFSSrZ7gYkWAhhQqTnkkbzt597DgqiAg7tYMdYH9S8Y15qLk5sbGa42XfZg3ed8vOf/xxVVVUoKSnBV199hU2bNmHHjh149dVXXT0veYWlKV69TH6shRsNROmshaXy4KnFTlBpJHQj97N6Li5AL+Rw7eOUXsjBubiAuf3//u//4tZbb8XQoUNxzTXX4I9//CNeffVVXHfddS72kjQWQiAsbqJee1uxnM9Ld2Mn3nAsWpNMHk/qvrCk/fELu/csD6W4G7ulzhX23HPPudgbY0iwpBnkTUPIiAyOFXbOn4dS6fN2+QEJljSD9+VhmWGzzPT8GjQMa31wRttrtQQvbC71mJZ0fKfnctve4vS4TgqEiSJdIvLdhmwsaYKdNeRUxu7yl1aIur2MFrcXyJ4dQDROlvKcJiGl98Q5JFjSgHhMil+zQL+XOLTwDDrqtn7ZLYLmiZYKkHBxBi2FEcKRTZBo4Z39+z2w+6mt+Jl6hwdRy2ip7n7sFaSxECmNXe1Ej3RJykkQTiGNJQ3oNkZutmynv6/1QCr7YKtNOmkET/13u1oMz71i7bcb53Yyazfb18i1V/ZniOCDNJY0gSWWgEhdaODmhxxe7EMaC5GE1zNJvdk4jzag3d9o3yB7VbHcDxG/G4umYQe9fUU/VyKPx1OKoO10F46fUYSd24oLeoVQmiW/PkCCJU1I5xmrlVChCpL+EtQ8bG2nuzD0zydxyju5gpwQsHtkH+mFCwmWNECmFPhxzAZyVi3E6jsrRAYfikYbHMkLSzCkW4Gtsj1rvKifv9moNmx3/IziqVABgFNK93lLs+zt39DQgJ///OeYO3cunnjiCaF9UyO32CMcQ2vE+kIjvk1m21O6aFF6AY2yXHuRMgNHh92Jzi+O+t0Vx7z33ntobGzE6NGjXT8XaSwpjB1Nxai9WZEtXvRm4/Ft2n9Fobfc5VURMC+Weoy8rZymcfGjYFj8fol+BvTOoUXvfIV/qUZ0hyvd8IyOjg7MmDEDzz77LB5++GHXz0caC+EpLDNRt2ar8ZmxX8teetcV15h4NCc790dWrUwPt38nnmVYADg67GUAwOk/H3ClP15QU1ODG264Addee60n5yPBQkiDLMsfdvAqn5cX5wi6jcQMp/fv9K69gnriHZs2bcL777+PhoYGz85JS2EpimwGe6vlILdmp2ZLeDyGbat7adfBwCyFiJltyOx4gPsZp0UujboJbwZrlvYJ4TJisN1uecbBgwcxd+5cbN++HTk53hUlI8GSgvghVILqMioCpzEkesJF7z4GZTAPKkZCpfAv1Ti5vw+yBp2PrJyz7ljf7GoDQuzVHP2gtbUVR48eRUVFRWJbZ2cnWlpasGrVKsRiMWRmZgo/LwkWggk7A5hZihSvAjFZkgrKYH8Qef0irofHiSOVqcc09EEZbsCLfnfFFtdccw0+/vjjpG2zZs3CsGHDMH/+fFeECmDDxtLS0oLq6moUFxcjFAph69atpu0jkQimT5+OoUOHIiMjA7W1tbrtTpw4gZqaGhQVFSEnJwfDhw/Htm3bkto89dRTGDRoEHJyclBRUYG33nqLt/uEYIJQKyRVZ/Wpel1u4IYm3XtEnvBjiqZv374YOXJk0l+fPn3Qr18/jBw50rXzcmssJ0+exJgxYzBr1ixMmTLFsn0sFkNBQQEWLlyIZcuW6bY5ffo0rrvuOhQWFuLFF1/EgAEDcPDgQfTt2zfRZvPmzaitrcVTTz2FyspKPPPMM6iqqsInn3yC0lIqDSoD2pfX7ssswsVVuzQnyu3aT2TsEyBnv7STHbPJj9a2opf88wSO4yiSB8zOXr2QExqMU0pISJ9ZyAl1p3WRHW7BUlVVhaqqKub2AwcOxPLlywEAa9as0W2zZs0afPnll9i5cyd69+4NACgrK0tq8/jjj+PHP/4xbr/9dgDAE088gd/97ndYvXq1p94OMuNlQS8vtBQnsSWUpiUZGQd/L2B5DtSTEB4N/KKsM9g98gwiH7Wj9whvJrcicoXt2LFDTGdMkMLG0tzcjPHjx6OmpgYvvfQSCgoKMH369MQa4OnTp9Ha2ooFCxYk7Td58mTs3LnT8LixWAyxWCzxORqNunYNMlCkzOjxksT/b4bT5IMiBnGjPjgVLiyk0qDrtu0qKPfKyKPOSDPRpvdRbzuF3tiPC1CIAfgS/5N0zNIsoD/OIOtcd2wVQUUKwfL555/j9ddfx4wZM7Bt2zbs3bsXNTU1OHPmDO6//34cP34cnZ2duPDCC5P2u/DCC3HkyBHD4zY0NKC+vt7t7kuJ27N19YtImoE/xAe/dDOos6D3TJotj+n93yzyvxiDcRifAwCyruiXcEHOumKIs46nCFIESHZ1daGwsBCNjY2oqKjAtGnTsHDhQqxevTqpXSiUvLaoKEqPbWrq6urQ3t6e+Dt48KAr/ZcRL6PMtecRlX9L9oqNRtfpVv4x7TINazp9p0W79DIDaLc7vV47kxP1/dD+G4cnbiq+zey+FmMwinE2fuUwPsdhfI7jV7TzdT7FkUJjKSoqQu/evZNc34YPH44jR47g9OnTuOCCC5CZmdlDOzl69GgPLUZNdnY2srOzXes30ZN0mT3LcJ08fXAzF5pTnGq8Wg3DSBMxOqfWtsLSn7i2QugjhcZSWVmJffv2oaurK7Ftz549KCoqQlZWFrKyslBRUYHXXnstab/XXnsNV111ldfdJQxwMtjKOujJigyCTUbsaj4sdHV5nCPfB9RjsBO4NZaOjg7s27cv8Xn//v0Ih8PIz89HaWkp6urqcOjQIaxbty7RJhwOJ/Y9duwYwuEwsrKyMGLECADAnXfeiZUrV2Lu3LmYM2cO9u7diyVLluCee+5JHGPevHm49dZbccUVV2D8+PFobGxEW1sb7rjjDrvXnpIEOfKdhIs+orIaaN243Tb028kUoN5HZH94HEy0bbOyvkFGxmkcPvxXFBScj6ysXjijswJ/Cp3oOnVKVJc9RVEUnD59GseOHUNGRgaysmwWfPkWbsGya9cuTJo0KfF53rx5AICZM2eiqakJkUgEbW1tSfuUl5cn/t/a2ooNGzagrKwMBw4cAACUlJRg+/btuPfeezF69GhcdNFFmDt3LubPn5/Yb+rUqfjrX/+KBx98EJFIBCNHjsS2bdt6uCUThBewRPQb4URQyK6p2Lk2dW0ct+DRZLRtMzKAQYMOIxKJ4fDh/wMQwgkc77Ff9PhJZO4PtlZz7rnnorS0FBkZzhazuAXLxIkToSjGN6+pqanHNrP2ccaPH493333XtM1dd92Fu+66y/JYRDJ2ZqZuVw70Sjtxml/L7N55dQ1WSSwB9wZlo2t0W7tRH9+JIBYVz5SV1YnS0qM4c+Y4OjvP2oJXYV7i/3cPehzAPzg+l19kZmaiV69epg5RrEhhvCcIv/EyiSbvOYyEhoxLhyLun8jrsiNUjIRRKAT07t2F3r3P2iF+iqWJtjn4BsBL334z1U53UwYSLGmM2SzXaq3brbVwq74Y4XUSRz8zDdvVOON9NkqZI/qanGqwRsJehAbCqslYTTj0PNCCbOcUhRReYUSwkH2d3w7xWJxUvDZZkCmQlreKJMEHaSxpgJ2Zo1mKFTv4tZzjxJ7Cu79oWGxjXpUfcAprHXuWQV1vf724FFaM2qu3k7DhgwRLSjEVkdCNAJwPMLIOUEQw8XJ5yEpIsLQ3287G5m+DUpsdHCO4kGBJMYqU5oRwsW7rrueXKNw6J2/kupogCt4g9NktAeSFxqHtexDut1uQjSWFiIRuZBYqhDXpPDCkCmRI9wcSLClEPGkjDYjOiZcgYJ3pBqGSZqqil5hTW6yLRcCwJKFkaRMnnR1BaCksRYgX+Tr72b/YB1FG8FRYfjJC1KDjxBU8aAMfj3uwWznDeI9/1uaZXrYWEiwpwWaTCGntA73Z/e6kCHbSkvhJfLCbjWpXjivDNfLCUxVSdNXR5InejWklXEiwpCBnA+DcfZCDrlHImIKGEIudHGG8rspET0iwpCjGQqU71YR26Ux2nPbVrVm3TLP5RDJHiF3ikuHaiGBBgiUF4dVURNtERAmsIAk+gE+DY0keaff6eVLTiz63FXa0XCMNwk4qfO2/LPvYRaZJh9eQYEkJxCS844mBCRqprKnI1Bc3Ya0OqYdVCWOzfQh+SLCkMcneQ82Jf7vjYay1DxEp22VLo8KCkwFHRruUDH3QQ3uf7WYqVu/vpOCX0XG1kEAiwZK2mC2XpbLmYgetRiCbZiBbf7yCRUg4EU6iPcS071Qqe4mRYCF00Wou5m3N06/LOiMWjezXKUPcCs890gY5amGxkWjbq49pZW/RbrcSZNrveiSxVJK/S2VIsBCGiNJcupPxyT3ompHqg0CqoicE9Gw0rIKLdynN6vipHDxJgoUwxeqhpyUzf9BLY0K4j1sBlKkGCRbCMaLdjFMZUfdIvYzjRV94a8O4/SzouQ9rcSIEzJbHtOewMvLrLaep7XapGJVPSSgJRxQpzSn3UgQFK6HiZ2JMt+05vMtYdo/vtC2L80C3EE6tVEshRVEU62apQTQaRV5eHtrb25Gbm+t3d1IK3howbgQGekGQHBOSZ8XOtAk71Sz1cHK/RMXrmBnZWQIojc5vpbVoXZ+1bWQvDMYzftJSGCEE7QtBthf/SaR4kcAbzG9YtQo3ShGbHVMtwLSai+yCxgwSLISnsAxyQbXZ+KHNWGkiovrEsp8It3MzpwQnmoqetsBzPLvBmWbuyj3ckQ36G0RIsBCuEJ9ppbLmEjTB52d/g3avtNgZ6K3sK1bCKsgaDAkWwlWsYmGcxsp4rSUEbYDkSUgp2m4i670SqQ3YrfNilRRTd9lMCY4WQ15hhK94pdF44R0li0YQCa0nu4pEmAVq8tt0NgdiFYA0FkJarAZHXptCfGYoIhOAUeyEm8JF79rcOJ8oocQSB8L7vZO+BN1ukbANBUBzIcFCBBbeAVDrdusEWQcp9fWxCFBZl6tEI+vvpYU1ZYyR1iKLDYYEC+E6MhjyRQoVWVALDqNrY/Gwc5IeJl0Ek1ewLokZ3/fNEFWfyQkkWAhfsWswFrV/umBW90YtTM4KIiunC/4sxX7gdklqt9HTYLTBnNr2MqSIIcFCeIZXmkt37YvUFy5OtQWzexQEA3E6wJMuJs5ZN2X/NBcSLIR0iFhe0TtG0JdtjPrvpRAVpak40SRSoUKjXjJLEciS3JLb3bilpQXV1dUoLi5GKBTC1q1bTdtHIhFMnz4dQ4cORUZGBmpra3u0aWpqQigU6vF36tSpRJszZ87gvvvuw6BBg3DOOedg8ODBePDBB9HV1cV7CYTPiEpcGU+y6GeyRT9Jx2tWYyaU3F5+E3F8tVu4tjopz/GN2p7VXLr/vNRCuQXLyZMnMWbMGKxatYqpfSwWQ0FBARYuXIgxY8YYtsvNzUUkEkn6y8nJSXy/dOlSPP3001i1ahU+/fRT/PKXv8QjjzyClStX8l4CIREUc2EPXqESd01W/5m3b076N47ebxX/Dd3+LeMTCKPKkkZpW9wQMjz3n0VYsEblW5UH0F6vX5Mu7qWwqqoqVFVVMbcfOHAgli9fDgBYs2aNYbtQKIT+/fsbfv+HP/wBP/jBD3DDDTckjrtx40bs2rWLuS+EXDiNug+KC6kbMRS8x+ON3Yn/LjLaWtxKle8WRkGQer+H1TWwVLGUIdeeNJH3HR0dKCsrw4ABA/D9738fH3zwQdL3EyZMwO9//3vs2bMHAPDhhx/i7bffxvXXX294zFgshmg0mvRHEF7jtgDk1UZk0BKNZtZ6g7C2MJb6GHrb1H+i0WpN2vMb9UuvrdEyrtmx4/sZfad3Pj8mYI7qsYRCIWzZsgU33XQTU/uJEyfisssuwxNPPJG0/d1338W+ffswatQoRKNRLF++HNu2bcOHH36IIUOGAAAURcHPf/5zLF26FJmZmejs7MTixYtRV1dneL4HHngA9fX1PbZTPRa56DYy+l+nQyacajlm7sWihEqRMsO1Gil2cfsZkEEr4q0Vo3Yhd0Lg6rGMGzcO48aNS3yurKzE5ZdfjpUrV2LFihUAgM2bN+P555/Hhg0bcOmllyIcDqO2thbFxcWYOXOm7nHr6uowb968xOdoNIqSkhJ3L4bwDbuDsazFu4yux25iSFaBYmdfPQHDk9LFqOSv2bnMjicaGQRKHBYXZPV98cMzTJqlMDUZGRkYO3Ys9u7dm9j2s5/9DAsWLMC0adMwatQo3Hrrrbj33nvR0NBgeJzs7Gzk5uYm/RGpiUiV3+9lojh+aWCs1x8JrU+67yL7y7rMk07w3mc/lzyl0Fi0KIqCcDiMUaNGJbZ9/fXXyMhIloOZmZnkbkyk5AATlGuKD1yzUQ1wLKoHobCVUy2FNe8XK1r7k9Xx/Yy+5xYsHR0d2LdvX+Lz/v37EQ6HkZ+fj9LSUtTV1eHQoUNYt25dok04HE7se+zYMYTDYWRlZWHEiBEAgPr6eowbNw5DhgxBNBrFihUrEA6H8eSTTyaOUV1djcWLF6O0tBSXXnopPvjgAzz++OO47bbb7F47IQla7zCrvFZOByEnVSzN9pVlCU2N3Rmr0fIgz/GsXGNlRkQ/WQWA0+OrUZejLlICFHm/a9cuTJo0KfE5bsOYOXMmmpqaEIlE0NbWlrRPeXl54v+tra3YsGEDysrKcODAAQDAiRMnMHv2bBw5cgR5eXkoLy9HS0sLrrzyysR+K1euxC9+8QvcddddOHr0KIqLi/GTn/wE999/P+8lEBLC4nosy6CkNxOU0S2X4MfqGTMqzOXkmOrj2qHnvlN9FSqAQ6+woMHj1UD4g1MPMbZzsM/G9b6zEixe9199Tidr6tpiYUbf8bTR4sbkQD2wOjH48wY9srbn0dy0x2U5jzaJqFtLYDzjp5TGe4KQEbNUNCLS1PgRJc0a+2K0ryh4jPXaf2XRZM1gNaRrBSTrtXUfe6rvWY3jSGm8J9ITL7QVgL1Gu19eNXbcjI2+M9NCWDH7TURpKUapWtQzdvW/PFpH3HPNjn3OSGMwcj5wS+tRnyMIkGAhCA3xAcjr2Z+MA4dXDgmsA7jVdqu2vMJFlB2FN32L0Xdap5L40pffNhUtJFiIwKHnbcMzWHjl2WVmC3ELp9qFnbZ2MEt3wuJN5WQZjDdwk8VYb/Y9q/E+lSDBQkgDj9sx4Gxt3UgbiYRuxGxUIwJ/3TXTFa12wdJO9Hn1trMkh/QCGV3a9SDBQkgFS5VJltmd3TQtshg/nRK0eJs4ImJfjEr2mtlxjLBrTGdtn2qaShzyCiOkhGXw4zGue2GEp1gWfZwOnrxFr8xsNXY977T7WfXJbQ+/+LMv60SIBAshHSwD9FmjZbMUub3sCBWng4/MmocapwOsXUM9IE4j0EuSaXV8N3KoxRFVhdUtKECSkBIr12P1bE3PLmMlbES/lKy2oeRAS/cEg8xLYXbT8rilAYjui9PgSSv80lQClzafILScfXE2f/vvVM33U3u0ZdEa3HohefoQxyshIxrWKPcg5wpzgrvX6H+6FhZIsBAphVlZVj+XzJyc2079FTcFFYvrrRV23MStDOJuJXu00xez/ez20e/EkjyQYCEkh/1FsqM1iMLsnDLYgEShTpMfgf+11dX4oQ3xCgr7fQyGphKHBAtBCMLPAES/qmDaPY8Ig7ZewkbAWwFjFswpqj9B0lTikGAhCMIWfmsq2gHbKKLeC0HDk46GFVnTtbBAgoVIScxsLV6f02kfRKTEFwXrtTi1wZih3t9uRLwogcPSF6tU+HopY4IeOElxLETK4YePv8wxBV7jVfoTnpxfbvXBzrn00tbYcWaQGYpjIVIWPYO62wLAy4Jf+uf3x9aih5OB3+7xeXBrELfTL6MyCTJNWCiOhSCgl9TS/ZeUpcQyQejRc1Igj1DhhQQLkdL48XKScOnGy7iSVCDIgkQLCRaCCAhuLXO5WTfGTZsBbz14wjvIeE8QAUUGLzGiJ24mnwwKpLEQRArhd2yJSGRyRGCBJa4mXSDBQhAC8dK2ImqglX3ATge6Y1mCFwhpBAkWgnAJP4I00w112hTW+vQiYK1jz9OP7lIRqWHAJ8FCEIx4qY2wBMx1xzmQ0IrDWp9ebx837CHpvBRGxnuC4KBImZH4M/pONDwlmFMJ9b1mua96RnN1lU5teWIRJYq157azLRUhjYUgVMQrV1oN5G4P9G4PQE6KdcmKmcZiR5sxQy/fl9E51W3TRYshjYUgNNRjGhqVl3vMmFm1Eat9RA4uTgWc0YCoN+t3c1B0Mz2L9jujypZGGo3e9euVVzYT0qxtUwXKFUakJUZ5xCKhG9GovGz54tsNKrSjCdipIOm0H2aZemXEy8qRIs8Vv5+y5QXTg3KFEYQF+qWLu4UNy+DJslwmM7IKCNlxR4AFqzokCyRYiLQjbkfRwjvr97pKo5kgI9dmc+xqGk4EMEtNmiBWh2SBbCxEmrE5LQffdDEaGyHn9U+VfvnLLqSxEIQEyOZlpm0vemAWbcPxo969Fdo+qa8tVTWVONwaS0tLC6qrq1FcXIxQKIStW7eato9EIpg+fTqGDh2KjIwM1NbW9mjT1NSEUCjU4+/UqVNJ7Q4dOoRbbrkF/fr1w7nnnovLLrsMra2tvJdAEAThC90TiNTVVOJwaywnT57EmDFjMGvWLEyZMsWyfSwWQ0FBARYuXIhly5YZtsvNzcXu3buTtuXk5CT+/7e//Q2VlZWYNGkSfvvb36KwsBCfffYZzj//fN5LINIYv6PVRRvNtfYXt2wtLP3Wxm2w7MPTlgVWzcXI5dhoGzk78MEtWKqqqlBVVcXcfuDAgVi+fDkAYM2aNYbtQqEQ+vfvb/j90qVLUVJSgrVr1yYdmyCsUBvrU8m+YnUtfg2KdlKqEKmFNDaWjo4OlJWVobOzE5dddhkeeughlJeXJ75vbm7GP/7jP+Lmm2/Gm2++iYsuugh33XUX/u3f/s3wmLFYDLFYLPE5Go26eg0EYZdUEHhGdhmtR5YXgk4b4OkU7THsaHDphBReYcOGDUNTUxOam5uxceNG5OTkoLKyEnv37k20+fzzz7F69WoMGTIEv/vd73DHHXfgnnvuwbp16wyP29DQgLy8vMRfSUmJF5dDSEIkdKOha3E6sAibpFjCcatOSVAG66D0UyRSaCzjxo3DuHHjEp8rKytx+eWXY+XKlVixYgUAoKurC1dccQWWLFkCACgvL8d///d/Y/Xq1fjXf/1X3ePW1dVh3rx5ic/RaJSES5oRJKGi9gyTJabGzeU0J55cXg/W2pT8LDacdNRU4kihsWjJyMjA2LFjkzSWoqIijBgxIqnd8OHD0dbWZnic7Oxs5ObmJv0RhIxo3Y2DHNXPipP8YzyCTkSeLt4kkkbX1p2xwbvyC34hpWBRFAXhcBhFRUWJbZWVlT28xvbs2YOysjKvu0dITnwJjPAXt5fheIWLG0uDdo6XDsKFeymso6MD+/btS3zev38/wuEw8vPzUVpairq6Ohw6dCjJ9hEOhxP7Hjt2DOFwGFlZWQkNpL6+HuPGjcOQIUMQjUaxYsUKhMNhPPnkk4lj3HvvvbjqqquwZMkS/PM//zP+9Kc/obGxEY2NjXavnUhB4i+sTEtgZgkr/dBMWGrJG9lFeAbSdFgGskrbYrR8tkjZlFIVI7VwC5Zdu3Zh0qRJic9xG8bMmTPR1NSESCTSY3lK7d3V2tqKDRs2oKysDAcOHAAAnDhxArNnz8aRI0eQl5eH8vJytLS04Morr0zsN3bsWGzZsgV1dXV48MEHMWjQIDzxxBOYMUOeAYTwm/RM10LIidXyWfezurnHdruZjrVakJ9Ci9LmEylEz5fULVgM7Xa0ES8EI4vGokW0EV9kUKQo9FyK7SauZN1PL0DUjmDRej+6kYaf0uYThIukg2HdC7yObeHF7lKe3QDR5KwJ+pMko2fPrAyEH5oLCRaC8BE/lu5ouVAfmRJZ6vVhETZxVTEF/JsEkWAhUgDvlsAAbwdmylWVPrj1XPmhuZBgIQKNTJH1RrYLu2WMZcCt2bte+hc/Ur8YYWUnYbGj8NhazMoIGDkBsFY6BbzXXEiwEIFFJqHiFrzuvUHWbGRYgopj1ReWvrqRo8wuerEzbmowUgZIEoQVaqEi04CkR5EyI0kAuikMZb8XspPK90/73LkZpEkaCxEotAGQQRoIvNCu3NJYvIqi9/P35MlGLCoPmNF9des+aF2S3YI0FiJQFCkz0Ki87CjPVKoS5GUwGeARbjxOFfE2TvKbuYVb6Y9IYyECjUyDqRONhMXAbyew0U+MXGbN8OP3dGKk57W1sF6faOGmh5uGfRIsREDw1qWYB788mfwus5wOpINWHA/ITM4m4cywT4KFCAxeveQ8dVHc7JNb9etZEGk/kHVwdtIvngqSbmNHM9Qj2f7iLEEm2VgIydkMmbUVLV7afkQInHh/3eyzTMuVTpHhWtzsQ/xZ6LZjhmzbX0hjIQKD3QJNbg8GD7RGkz9XsCc45Vnf1s8HZd/uoidMtJ5Rou6djNUU1ddmFaCobst6T5wUFFNre+p7p/2/3j56GGm/etcdf54fqMi1PXkhjYUIBE4GpSB5kPlpM1HfI5H3S/b779Q47wbqe2b0u8TbWC05Gk1erK7tgdbot202c2supLEQgcDuzNnpWnMcFs2AR1NhOX/8nG7ZWvTSqqi/k1kYiERPOxCtsVnBGkOj1x+z9nY8vrTPcT2mYZGyCdGowQ46kGAhpKJn2ongeT3JsA7Pi1HFyHSDt7a9W+c3+z7+fPF6BfK+S0lLvBW5uBdrmPelpTBCGuJpWtR/dl5wrwcF9QyPV6g4TfUiIlWMjEtBhDmsmkj8mWCxrVjRgNuY25LGQkiBUUJJO7N/LzQG9WxRa7znhVUgGC2JuaHVibyHon8PUe61bhzDK7qTSnY/D1b95kl/lHSsilzV502I4mv8O6NwIcFC+E46ZCkm0hszrzO7OPUAtGIRNmlKHEcBEixEUAiCUDFLf/FARS631sLiDOBWyo2gLG/ZKfHrp9Zh9psaGeX1aqyI/n3sHs/Jc0eCRTB6bnl+1JwOBsEJfDRDLVRkGOAIOWHJROCHUHHjWSXB4jIkVNxFpoH8gdaoMJdjNxHtZuxmckyeIEB1O5a2fmEW/Bjfrm0vyvWZ5X6KyBlGXmEuQkLFiGClaeHBqSHfb2RbJtMGV9pZHpMRo+BHluUyu/B7LNofv0hjcQkSKt7g9azUbDYeBG1FFHbX390OOvRTS+FJWKrVDPU++wllN5YEN8t8eoHWMyvZG4Rgxe8BIQh4GdEuK34HpLp9/0mwEEkk1l4V9x48qiMiB3pr+U4wC8LTpkqxi98CyY5NTy1I9a5fz84iCr/uFwkWhwTRC0y/z+4P9EHX6oxIpyUwI4yCW7W2BKczc7+1HbuC0ez6ZbYF2YWM9w5Rpx/p/hwcoaLtu7tFq27scT4vcfvl5TXa+62xOV3Pt0olE4SMxiyGc+0+ev8nekIaiwO0dgm/Bwsz4gLFaBAw30duYRlHr/KjFwOAXU8wv54XvWUXEVmggxTQGNc84hoQSz+0y1milxL9RmQgLgkWmwQhDYlamDhNcOi0H7LfK4Kwws9S0V4hahJJgiXFUb8EfkTh+i1UxF9PT63IiY3FTnCh3sxS5uVF0WlKRGk7PHXredv6JYT0+uBHP0iwCCD5B5zqQw96Bhv67XlltvTmFuqMr14QCa3HbFQnzp3qqAdL1kmKG0uRfhUhc/obW3mEscBqG6vHtMSz6QckWGxg5N3khy0irhFoZ1YsKejdfjn9GGxFn5NVUIkS5H7OMs3cgXm0Az3bjd1nT68/eh5Wbtk3RL4zZh5hIvrvR7JJI7i9wlpaWlBdXY3i4mKEQiFs3brVtH0kEsH06dMxdOhQZGRkoLa2tkebpqYmhEKhHn+nTp3SPWZDQwNCoZDusdzGrstsJHRj4i+e0iR+LO029Tm0+2j/3B58vNQA1LB6FUVC65P+CD60983snvvh0ee1ZiLqOXL7XrEIvEblZTQqLzP1o0hpFjox5tZYTp48iTFjxmDWrFmYMmWKZftYLIaCggIsXLgQy5YtM2yXm5uL3bt3J23Lycnp0e69995DY2MjRo8ezdt1x1gt77AIHe2+ep5l3Q/3jT32EZ2cjiXbqkykkuBguRajNiKWZIy2Gz0L6pm1CG8o1n1Yl4rcqHdiButxrfpudBwjrdHqvFoPRb9irLgFS1VVFaqqqpjbDxw4EMuXLwcArFljXDM5FAqhf//+psfq6OjAjBkz8Oyzz+Lhhx9m7oMIRAX3iRwcRQWdWSHK7ZhH7Q+662ZQ4HXu8HoSYjTAutUPkVqGLBM2lvdOdGiBNDaWjo4OlJWVobOzE5dddhkeeughlJeXJ7WpqanBDTfcgGuvvZZJsMRiMcRiscTnaNRevIGbnk09NRjjNXaz2Y2TgdhsX7eKTYnGj9/Ha0Rfo3bAkVGYm9lYzOxBTqLjze4Dr63JKDU+z/68bbWF5/wo5yCFYBk2bBiampowatQoRKNRLF++HJWVlfjwww8xZMgQAMCmTZvw/vvv47333mM+bkNDA+rr693qthR4OSvSam28sxsZBy7ZEO3ZZjbp8Mu7igezDMBWy3Z+Y5QaH7B+F8wEqhl6QoUVkTYWKVK6jBs3DrfccgvGjBmDq6++Gv/5n/+JSy65BCtXrgQAHDx4EHPnzsXzzz+va3cxoq6uDu3t7Ym/gwcPunUJKY/ebDlVc3+lCjwpSoKAV/11ou342YcHWqMOskCI9WiVQmPRkpGRgbFjx2Lv3r0AgNbWVhw9ehQVFRWJNp2dnWhpacGqVasQi8WQmZnZ4zjZ2dnIzs52vb92g9ri283qnVthNOvkmfWynks7mzZ7GL3yHhJ5Hi+WuZzUuucNpgyCRuIWvNmH7e7Ls49dl23AXNDYFSZn45LEh0lIKVgURUE4HMaoUaMAANdccw0+/vjjpDazZs3CsGHDMH/+fF2hIgKvZuRq4cI7UPo1cOg9jHqebIR7sMTOiFx+9Du/Vzpj5T2mXQJjxa3YO27B0tHRgX379iU+79+/H+FwGPn5+SgtLUVdXR0OHTqEdevWJdqEw+HEvseOHUM4HEZWVhZGjBgBAKivr8e4ceMwZMgQRKNRrFixAuFwGE8++SQAoG/fvhg5cmRSP/r06YN+/fr12C4SLwdHvyPl1f1Qoz+jJqGixsn1mt1vtx0nUlVAyHJdXvbDSKhojfZeOZ9wC5Zdu3Zh0qRJic/z5s0DAMycORNNTU2IRCJoa2tL2kft3dXa2ooNGzagrKwMBw4cAACcOHECs2fPxpEjR5CXl4fy8nK0tLTgyiuvtHNNwqAZGiEzXgvwoLwHbtVskWk84Fmp0Ouv21lCuAXLxIkToSiK4fdNTU09tpm1B4Bly5aZBk/qsWPHDq72siFqUDB6yLVlhs3gtcd0u1/3fDBFXJPeoGDWP5HJ/vQdFNgDGUWndDHCS4EiW2Etq0qUduxKZoHCfiWTZE00ate+4jZSeIXJyuxQtRSzk3QjXQ3OsuLn78GSJ8ysvSgiofXMdVtkw4+ia1Ia72XEaapyt2azfH1oThkXYa+uxczOoe2DWO2J7frspN13gh9JMnkTQbo1+KvtoG4vi7HEM5nZVfzWZEiwpBFBECqsL6oM1+JmH2S4Pi167s8yOmr4vXwnCp5gWbWRXk+oeH0/SLB4BMvMUuTsk2etnsfG0j2YeFsegKWfftgm4vdBRiHAglH8hlpgWA1IfggXI5uI07gdvf2tnj1WIcaTmkXv/FaYaSh+CFmysRgQ1MHCbeLGe0Jd0iD10JYjkD1XHOCNLUjvPjjJcCBy0DfKB6ZnY3H7uSXBooPeTdfOHFKhiJXbxyXYkeU3CIIAcQvRRbzcPidgvQSmJdlb1D3hQkthBlhltfVrfdntFCZOl9/i1SxFzsSCNtjZ6a/Zb2GV2kXd1g5OfitRz6OeUwBLosY4IrQVqyzfVr+r02U47vbfChU9TcVMyHiRsZwEC2ELo9kOuQqnD25NrGR1CpCtT6xFwvywsZBg0WBUe6VnAka+h0y0Id7qeG4G+5kFX9o1oDqZhdu9VhbYnALccX02cuZwem1W5YfN+qD9TtRgq32/vBAuTlyGzfaRYXLlV+XIOCRYVFgV9JJtxiIjkdB6LFKC7+rJQ6oa8NXwZHIQcY5UwK/s0jyCUnTlyDgkWL5FK1TM6svbmenwpEt3cjyzmb9eG9Eus34PDl7ZY/S0FPU20VqM9rcTcZ9ZBj6r86g1Cz+CJ/Ww+46KhkeoyNBfkaS9YImEbkSj8jKgAItg/DJ4VV/ea0QPfjK/IGLjhHret+QofPv3VX+C8zIAYDaqbR9Xi91nWXSVS9GIfEdFPjN6glyWmjkpW/PeL7oflJd1v+Od+fA8hDw2BRYbgl8vutOZqQgPJ6fr8SJT5djVVPTOxzrgiA7SY4Hl/rCcT70S4AZ6768Xkx+9FQ5tMk29/6cKaS9YzGwCTl9EWb1b3CQVXxIrzDQVtfu1dhnLajKgdy8jSN7HSJP243dgedZZ69aLxol25uR8MmgjLJCNxQXMBIDZC8rjZRVH1Nq70eDkpyAz64uTfrrld+80RUwckVoK7z5G6U3srO/r4aTEb3x/I0HixTKQdtXBLOW+Ft77EhQh4gVpLVjOLl+kl1ZBiMXMiB/XiEUKRZZgQlk0AbPvveijVqiwnjedhIQbuf/SNqWLlynXvT5vkEmFtDXa39hNe5hfebz8qPHhBFEanMh9ZMCthLIhxaq8YwoRjUaRl5eH9vZ2nMy7xRXDsxO8rkBo1tZOX5zcD5bzWR3f6z6fPa/95U27Wa5ZlxaDNuDZtQ0ZGeRlvX6/bZF2spSrx8/cXPMAzLTVWPzG69Tz6UB8Fs0zmIgR5uK0UNGTFZYBzO9BTnQfeLU4nnvk5H7KcJ8Bb0pfpLWNRY0dl0cnRuWgLInxaEDO7odzt2O7xmA3U8JYIUoLMwuctHqu9ewQXuPEVmMGi5Gdx+bihtE/FUlLjeVI3lTdIDRZ1Wa3SRXtKRJaj9mhaldfXr/vlVObilmNFVmff15X6iJlRg+PRFETBdF5xVKVtNZYeGYf2ofD6EHlXW93ksZFb387tgqn2hOPm7Goc/iBWqgEvXqkGh7XWTcGSZFxLSLjefQ0ODvanawC203SUrD0b98M4Le29xclVBqVlwG8nJIzmsQL6NDVNn4cq1Qm7nmTqV2HzQMhu7e7IwCNJhV+Clyz3GB6S3Oi8pzxDtSs5+MJ3kxHYcFDWgqWOCwzNTuZQs1Ifsj1U8nYwe3gQSd2CNEDoGghYh4oyaeRiL5W40nMWbsWXzAn+yDL0y+rPmhT4hv1R3TxLrfdjHkwSumi1ybok800FixTPQuQtBPVz3Mc2RCVOl99HG0qExHHFklCswqJSxIZx4uU9TxoBYNTmw++DXiIp70R+ZzLkuQx3UhjwXJ2mYP3YTZqy2IDkSl2xClm18br7WTUXpZU7E7hscmYLamylltwa+ar93u4tSSnTldjlI7FKmMwayJMNWbn4RFUeulk7CCyVIJXpLVgAZJfeLd/OBlmmzIQpBfEirPXYr6sybOkpm4r271ywzU7PrDHj6M3kBoJCr3B2skAHj+n0XnsLq05FSpO0avB5CZp6W7sFTxLGH4OIF6em6c0gB/9cELQ18X9Qu0OrB70vJyIqc+l9zvy/LY8wZROzsNLkdLsmbt82mssVvAsKWg9g3iCC/1GhFeT1TF469O4jdNrVl+PyAFBdPXJIOHHdddjWpJN0KkWxKLdmLlz6z1LsowTrJDGYgHNQvmJB6hp/1IFvesRGYQnYnAV8dxqgymNAiu9wo7WwIqddEC8fTHThMz6a/XuyOickLZJKPWTqG1O/M+Z27GxEZ/HeC/KcM2jQdjRNozQGl15+sibykS7nx3jNY/7tN19eYkXCgOcCQs7KYtEPgtWxzRCz85iF7uGdy9xayIrIj8YTxJKWgpLYmrCiC+bm6JoN0wvEJm7S1aC4pBhZ8Dy2g1f716K9IjyK6bFjkdZ0CHBoqFbqm829T5xMqtwO0Kb9fx2v+eF9V65NYgZ/W52tMGzM77NphqA6N/Yz+VYO+662n3N+s+jjbEchxfRAZl6sNhcUm3JndvG0tLSgurqahQXFyMUCmHr1q2m7SORCKZPn46hQ4ciIyMDtbW1Pdo0NTUhFAr1+Dt16lSiTUNDA8aOHYu+ffuisLAQN910E3bv3s3b/bQmSJqAHVLt+lLteoBk+5ved0bt44i09cQDMoOidQYJbo3l5MmTGDNmDGbNmoUpU6ZYto/FYigoKMDChQuxbNkyw3a5ubk9BEVOTk7i/2+++SZqamowduxYnDlzBgsXLsTkyZPxySefoE+fPryX4QirWZbbxZtYYHlZjGqUBwntPWLxxHHrGv0UBCKfHTUs98rIHqkOcHSL+BKxnubhhTbiFDvPotlzrPedH4KTW7BUVVWhqqqKuf3AgQOxfPlyAMCaNWsM24VCIfTv39/w+1dffTXp89q1a1FYWIjW1lZ85zvfYe4PQfCgZ/BUpyFJJ3iEl9o2wmpbMLKxWAlJMzuM1f5al3FZBVDQkMbG0tHRgbKyMnR2duKyyy7DQw89hPLycsP27e3tAID8/HzDNrFYDLFYLPE5Go0y90c723HrofNTDXc6c3drliwCo3QgcYyWXfQC9cyOL+qagx67Yub9p/cb8KQ6YXVc0bazCno00tjNUrmYaWFGKVyM9rH7/hllYTZ3Wfa2jpAUgmXYsGFoamrCqFGjEI1GsXz5clRWVuLDDz/EkCFDerRXFAXz5s3DhAkTMHLkSMPjNjQ0oL6+nrs/6hlQuhcBCyp6L57MmWNFChWnws7IscHsuOp7vAibTJcojd4lPacH3muxSlxqJaRYUrkY9d+ojVl7luqeernMzPoqA1IIlnHjxmHcuHGJz5WVlbj88suxcuVKrFixokf7u+++Gx999BHefvtt0+PW1dVh3rx5ic/RaBQlJSWW/VF7/wQVv2t1BJWg3zeRQoUF3sGMZTDkSfJqp50T130vB28REyIR8St2kEKwaMnIyMDYsWOxd+/eHt/NmTMHzc3NaGlpwYABA0yPk52djezsbEd9ETW7dZI9mSVrsp3juLV05UZ9EF6cBEaKehG9dC134z6aZU02w8qtVm+pyWrwFHEPrY5htZynbcvSxup+mR1XbznNClm0cSkFi6IoCIfDGDVqVNK2OXPmYMuWLdixYwcGDRrkej+CGJRI2MPNWR2LFuSnjcXL0gSilm9YDfrx/7NcF29frII3RWg3sggKXrgFS0dHB/bt25f4vH//foTDYeTn56O0tBR1dXU4dOgQ1q1bl2gTDocT+x47dgzhcBhZWVkYMWIEAKC+vh7jxo3DkCFDEI1GsWLFCoTDYTz55JOJY9TU1GDDhg146aWX0LdvXxw5cgQAkJeXh3POOcfWxbOg91CKjAaWQWuwC0+tGRkR3V/RGqFXdThkWv7jcYHnaau9PhZNgMWjTO++uS0MvHDjdgp3gOSuXbtQXl6e8NiaN28eysvLcf/99wPoDohsa2tL2ifevrW1FRs2bEB5eTmuv/76xPcnTpzA7NmzMXz4cEyePBmHDh1CS0sLrrzyykSb1atXo729HRMnTkRRUVHib/Nm9+wgVmmmZXkZibPwV+V0P5V4EJ4TkYlCvUobb3cAN3JLNkspw3MsPYyCOkUEfMqo1XBrLBMnToRZ3sqmpqYe26zyXC5btsw0eJLlGG6inrnoPRgyLJexJ1A8u+TiZWU6O0sRTtwxjXBrkLczKAFnfw+zZTC3bWNmLrJOjsd6HK1dwzjwOHm5kMdlWY3XcUhejBmyCRcpbSyyoZ7RFilTdQcBmV1Z1cgaK+GFWu+Xh4zZUpNXvwera6vX6J2T916xeJZpjzk7VN39H+VsezsC3KmQkGFS6gYkWGwQhGA2nuqVooy3PF5QdrUTI88bN72JjPrBM4lg/T3cSMmvjTExgmXtXvTESXs8nmvVC2I2Qhv8qvf+mj37esfW9tVIc+KJmzE7p3aFQeZJLAkWm2gfTjd/5FSa1dhxyTTa12ibHqI1Fe2ykZuOHE6N63aFtszoaQqswl7UpJAnT50d1M+Xk98/fr1eauskWBwgs+bip8GYZZB1IlD8sKFo0Q5iLLEbVjNqu/2In8PJvtq0RX4s7dotkBfHqq88Apr1+nnsSX4kffWrTAeVJk4BeAz3ssCTKsdekarmpD8RxI3s2my6Vmiv1e7SiFsELW2RkQcbb//djD9Jd0iwSIo6GlkUVtqVuqyvTC8X/4DhjgAV6ZLLe5xUWg51ggiBYnQcQhy0FCYAK7dd1pQsdiJ5RajS2vPJYhSUcQnMCqv1cL3EiMDZ/s9GdaKN9ndJpcFQ91lW2GwJTic96gmUHkZLgnpOLjz1apy8VyJ++25t25tVCxIsDon/UEbagJ6Lq5XmEAmtx2xU+zKQyJwK3wo/l/oSA48yFUXKVMa9zgb3GrtCBzcRqki81BRZBFdQ3xOvDPkkWASir5k4/wGdeFI5RatF2U2IaTU7tHs9smgqgL0U736kU2GZqbt1Pr2ofLNz6nl/mblFs2gHblyjWTyLDCsAXhvxSbAIws0ZgFFhn1TAbvS0Gr+dEpIHTlZt5Wy/2TUc4/PaQesN5tdzJcPAq8YsHkWmiYzMkGCRCDPbil6xH70XUr9YUnPS+qp2KU6Wl0W2AYYXL/pvVWRKZB9EHctuIKnI4+rta5WyHuh5v/1e8nL6O3slHMkrzAd4Hk6zWSTrDPNsbjDznFSEPt33zNjWsQibvk0RYk/zMGdqYiBwKwDPSlilIlbLb6mKV78paSwBRT1jET0LcSPw06jWud2qeF7CUvvD3XouzQA2M9sNzIpVBVErFJ0YU4taezF6ttyKt2I5j6jfjCWOShQkWHxhaiLQzgq2FBWi64qIFSpG6Tec4rdtRZ17yn30k592n//svbW6r6mshZhhZsNTb/d7qUsP3mzRRsT37X4f3dCuz0KCxSfis1C7+KWu233xRKQK93OJwmjW7KVw0ztXXNhoNULZBQiPFiLS1iNiaZl3f6891Mzw6h0iG0sawToIih4sRUasdx/P/eJcVviVgl9L97KlvSJRQVwWc0qQr1lUMHT3u7jZVXsrCZY0gvVBEvXAuZF+XYbBXBahEseuoJVdq/EalsqXTqpjspzb7ve81GOaq8t+IcXP0oweE41GkZeXh/b2duTm5vrdHYhcCnOr/ryTh49n4GJN1+LHgK4VtDIJFTVWNhi3BEnQtACr+yA6UNTIkYL13G4IlbPHZbe18IyfZGMhAoGfg7msgkSLUT95HEVYB1Qvsz94hQzX5EY8kh/XRBqLr2w29PiwmzpFj57aDb87sajYGyOsKvQFZXCXESPBImrA8VpjcatWjNPjitLQRVOPaXigNdpju1KxDaSxEMIIUpAkCRTn6CVBldGtlhCD31oXQMZ7qZCtDooau7XI9T6znCuVo5/9xkhYs/xustlTRL4zi7DJlTQ2ftyzB1qjSX+J7RW5iT83IcEiKaJddM3O06i8LPRc2hfdXiEm/12K0w2W302miY9MfdHiR9JYrZA1Eh7upiDqhpbCJESdesNN4VKkzDCMOjbTGHjXos1SpGuPQZqKd5iln49/75Y9gweZBYgavUSxetvdJEk7aY0aCheqx5KmeJXXifccdl5y9mSZXqZISXes0wrJPqDL4MXlJ3rXHhckagHTo6qpy+lcABIsUiHyRXFr5s/aR6PZrtG+pKl4D29ySytE5bSycz63z2FHG3HzHsTP38N+8u1ntabiR0AvCRYfiYTWY5FiPejKBGsf7a4xk7biD6JL7QY1k3KQ0LoQa4VKXFPx450i472PdP/gUwMxWyf31NQm7iwR/wvCMykDbpVytuvtptZagKm+TdRIY5EEtfbileairWfPiogIbb1gUNJWvOfsvddfd9eWLXay9GN3qYy1eqoo1OWa1dtY+iYC1lpFWntK/HN34KO/2iIJFglQlwwOgmZgd91dD9kSOqYbVvdexCTHrVm9FzYMvzC6tiR3Yp2lMN5oercgwSIxTtwV9QQUz/KGkYBjmZWyuqnScou8GBn27TyTsrgt82CmaetpNH6g70osx/0lwZLmsGpIyXm79PfRKzJlNqCQpiI72pmvvWzcfg/AdrCqNunXNWkF2uxQtZTvEQkWCRExq2PRBliEihOtIiizU4IdL+Ke/EIveNdL2worSfZJyKn1c3uFtbS0oLq6GsXFxQiFQti6datp+0gkgunTp2Po0KHIyMhAbW1tjzZNTU0IhUI9/k6dOpXU7qmnnsKgQYOQk5ODiooKvPXWW7zdlxqtl5jbucPiObnUf27TfY6pkGEdmHAXu89u8jNy9s/rpVOZhWL8HsmorQA2NJaTJ09izJgxmDVrFqZMmWLZPhaLoaCgAAsXLsSyZcsM2+Xm5mL37t1J23JychL/37x5M2pra/HUU0+hsrISzzzzDKqqqvDJJ5+gtLSU9zKkRru+7eX6tNXLq9Zy1P0yewnJlpJ6aD0K9Yzpop9Xo/dCix2tyusKjnb7ERQPSm7BUlVVhaqqKub2AwcOxPLlywEAa9asMWwXCoXQv39/w+8ff/xx/PjHP8btt98OAHjiiSfwu9/9DqtXr0ZDQwNzfwjxWAkV2V8CgpV4GphmFClTE+UX1L+/iEmE+fNinYrGDn5rJ1bnT84fKP/7JI2NpaOjA2VlZejs7MRll12Ghx56COXl5QCA06dPo7W1FQsWLEjaZ/Lkydi5c6fhMWOxGGKxWOJzNNqz2I2sdA/IyS8PTxxAkTKDNAVCOFaDmheDXrxQnddxX35Tj2lYFJC6jFJE3g8bNgxNTU1obm7Gxo0bkZOTg8rKSuzduxcAcPz4cXR2duLCCy9M2u/CCy/EkSNHDI/b0NCAvLy8xF9JSYmr1yESp9HPYmaO3qTuJ4JJ/BmdHarumejQA+LnS3UnkSAKTik0lnHjxmHcuHGJz5WVlbj88suxcuVKrFixIrE9FAol7acoSo9taurq6jBv3rzE52g0GjjhYtfF0/m5ZyT9n7QfQg+1hmIUve/mebXLYqkkZIIoUOJIIVi0ZGRkYOzYsQmN5YILLkBmZmYP7eTo0aM9tBg12dnZyM7OdrWvbmOU6sWOQV+rfYgImNQj3icSRkTQ8UpQWTkkmNVWkREplsK0KIqCcDiMoqIiAEBWVhYqKirw2muvJbV77bXXcNVVV/nRRc+IuyAHjbjRPgiGRoIgxMKtsXR0dGDfvn2Jz/v370c4HEZ+fj5KS0tRV1eHQ4cOYd26dYk24XA4se+xY8cQDoeRlZWFESNGAADq6+sxbtw4DBkyBNFoFCtWrEA4HMaTTz6ZOMa8efNw66234oorrsD48ePR2NiItrY23HHHHXavPVC4kWI/roXoaRZ2E1QShB/o1fzhrXDK807pvTNuvitB0lYAG4Jl165dmDRpUuJz3IYxc+ZMNDU1IRKJoK2tLWmfuHcXALS2tmLDhg0oKyvDgQMHAAAnTpzA7NmzceTIEeTl5aG8vBwtLS248sorE/tNnToVf/3rX/Hggw8iEolg5MiR2LZtG8rKyngvIZCwJKoMWj4mgnCKViOOhG4EvnWcSrWaMEG6lpCiBMR/TQDRaBR5eXlob29Hbm6wZgBxnAgWVptH3J2z+//mpWv1giP9LDBEEPVIdujhGZBZgy7d0ljMtKbuPvi3LM4zfkppYyGMMSsOtgibBOUZu5G5bZA9V4jUZBEUzA5VJz7bqXoqCt60TPF3WO89DtK7JqVXGGEfrzyxrIp3EYSfFCnNWITN3IO6FqP9WbQTp4JAhtT8diHBElDUy1XqbepYAh7NwwhtBgCWwl3xvhCEn0RC6zEb3ZpLBD0zWRjhZDC3WyXTiKAKFxIsAcbu4M2bLl8tXKxTyZBAIeRAz7DP8uzb8RKz056VIAoXsrEQBJEW8KZJsmv0F2XrDDIkWAiCSBuc5uDTQ/TyVypAS2EpjNYOY7QM4HWwF0HIAEvcl5Wg8GKJKmjLYABpLCkPpVUhCG9wQ1sJolABSGMhHKDWdEjDIYKCthKl7DzQ2l1H6oGK3EShNdkhwUI4IggPOUH0xHklSq024YWtpVF5GUAIiyB3whQSLGkEj9GSUt4TqY7IekdeuAQHSXMhwZImyPwQEoRfOEncqt3HTeHyQEVuQrAEATLeEwSR9ugJhEhofeKPFbdiWIJW6Is0FoIg0hZ1skqetC9OsJvOnzQWgiCIABB3x4//GWknvJqLFTxLZnFN5YGK3MBoLaSxEARBqFBXT3XLiYVFY4kLH7WmEpRaRyRYCIIgvkVdqbUe074tBz41sU0EzqL5pyZlMJcVWgojCILQIa4dqLGzJGan2FfQIY2FIAhCQ1xzUWsHam3Gbeym7pcFEiwEQRAuYlcDCapQAUiwEARBSEGQBYkWEiwEQRA+ohYoag+weLR93MU4SLYXMt4TBEFw4FUmb7WQ6Q7klN8bLA4JFoIgCJ95oDWalGRS/e8ibJI+bkULLYURBEH4gJ5NRZ1sMkgpXLSQxkIQBCEJaptKkFK4aCHBQhAEwYHbtYq0Bvsg1kYiwUIQBOESZlH3ein21RpK93dTA2dfAUiwEARBSMUDFbmB1VTikPGeIAjCJXhiT+LayuxQNaAEI9mkEaSxEARB+ExQjfRGkMZCEAQhED2bipXmoo1hCTqksRAEQUhAqggVwIZgaWlpQXV1NYqLixEKhbB161bT9pFIBNOnT8fQoUORkZGB2tpa0/abNm1CKBTCTTfdlLT9zJkzuO+++zBo0CCcc845GDx4MB588EF0dXXxXgJBEIQn6Hl+GRHkgEgt3ILl5MmTGDNmDFatWsXUPhaLoaCgAAsXLsSYMWNM237xxRf46U9/iquvvrrHd0uXLsXTTz+NVatW4dNPP8Uvf/lLPPLII1i5ciXvJRAEQUhFKmkrgA0bS1VVFaqqqpjbDxw4EMuXLwcArFmzxrBdZ2cnZsyYgfr6erz11ls4ceJE0vd/+MMf8IMf/AA33HBD4rgbN27Erl27eC+BIAjCNVg1FG1W41QSLtLYWB588EEUFBTgxz/+se73EyZMwO9//3vs2bMHAPDhhx/i7bffxvXXX294zFgshmg0mvRHEAThBJHZjePJJ9U5wlIBKbzC3nnnHTz33HMIh8OGbebPn4/29nYMGzYMmZmZ6OzsxOLFi/Ev//Ivhvs0NDSgvr7ehR4TBJGuqAMX7QqZJG1FU3clFfBdsHz11Ve45ZZb8Oyzz+KCCy4wbLd582Y8//zz2LBhAy699FKEw2HU1taiuLgYM2fO1N2nrq4O8+bNS3yORqMoKSkRfg0EQaQP8RQrkdCNzPsYpXXRZjNOFeHiu2D57LPPcODAAVRXVye2xT29evXqhd27d+Piiy/Gz372MyxYsADTpnX/QKNGjcIXX3yBhoYGQ8GSnZ2N7Oxs9y+CIAjCAr3qkOrPwLdJJxHcVC5xfBcsw4YNw8cff5y07b777sNXX32F5cuXJzSMr7/+GhkZySahzMxMcjcmCCJw6AmVVEjlEodbsHR0dGDfvn2Jz/v370c4HEZ+fj5KS0tRV1eHQ4cOYd26dYk2cdtJR0cHjh07hnA4jKysLIwYMQI5OTkYOXJk0jnOP/98AEjaXl1djcWLF6O0tBSXXnopPvjgAzz++OO47bbbeC+BIAhCGlJpCSwOt2DZtWsXJk2alPgct2HMnDkTTU1NiEQiaGtrS9qnvLw88f/W1lZs2LABZWVlOHDgAPN5V65ciV/84he46667cPToURQXF+MnP/kJ7r//ft5LIAiC8AQj20qqE1IURfG7E14RjUaRl5eH9vZ25Oam1gyBIAhviYRutPQKq8c0XXdiPQ1lEeQeinnGT99tLARBEKmGVlPRi1GJC5zZoW8dl+SWK1yQYCEIgrCJXkyL2fJXknvxt1pLECtEWkGChSAIwgZG8SzqlC5xIRMXImrNJRU1lTgkWAiCIBxQpDQjEroRkdB6XZuLWqio41WQAm7FRkiTK4wgCCLoGNWpT4quT6GcYEaQYCEIgnBIkdJsaCtJB0GihQQLQRCEQCKh9bqaywMVuXigIhdKxTZDzSZVIMFCEAQhCDPNBYgb9qempCeYmrQy3sdjQakuC0EQbvIVvkGf6Nc4BQAd3ePNqSgQxdcAgjn+xMdNlpj6tIq8/5//+R9Km08QBOGAgwcPYsCAAaZt0kqwdHV14fDhw+jbty+++uorlJSU4ODBg4FI7xKvJUP9dQfqr7tQf93Fi/4qioKvvvoKxcXFPTLNa0mrpbCMjIyEpA2FQgCA3NzcQDw4cai/7kL9dRfqr7u43d+8vDymdmS8JwiCIIRCgoUgCIIQStoKluzsbCxatCgwpYupv+5C/XUX6q+7yNbftDLeEwRBEO6TthoLQRAE4Q4kWAiCIAihkGAhCIIghEKChSAIghBK4ARLQ0MDxo4di759+6KwsBA33XQTdu/ebbrPjh07EAqFevz95S9/SbRpamrSbXPq1ClH5/azv9p+hEIh1NbWSt3fQ4cO4ZZbbkG/fv1w7rnn4rLLLkNra6uU/T1z5gzuu+8+DBo0COeccw4GDx6MBx98EF1dXZ73FwBOnDiBmpoaFBUVIScnB8OHD8e2bduS2jz11FMYNGgQcnJyUFFRgbfeesv03H72V6b3jaW/2n74+b6x9pf3fWMlcJH3b775JmpqajB27FicOXMGCxcuxOTJk/HJJ5+gT58+pvvu3r07KSq1oKAg6fvc3NweP2pOTo6jc/vZ3zjvvfceGhsbMXr0aNPz+d3fv/3tb6isrMSkSZPw29/+FoWFhfjss89w/vnnS9nfpUuX4umnn8avfvUrXHrppdi1axdmzZqFvLw8zJ0719P+nj59Gtdddx0KCwvx4osvYsCAATh48CD69u2baLN582bU1tbiqaeeQmVlJZ555hlUVVXhk08+QWlpqXT9lel9Y+lvHBneN5b+2nnfmFECztGjRxUAyptvvmnY5o033lAAKH/7298M26xdu1bJy8sTfm47+4js71dffaUMGTJEee2115Tvfve7yty5c5n76nV/58+fr0yYMIGrf1q87O8NN9yg3HbbbUnb/umf/km55ZZbPO/v6tWrlcGDByunT582bHPllVcqd9xxR9K2YcOGKQsWLJCyv3bO7Xd/ZXnfWPor4n0zInBLYVra29sBAPn5+ZZty8vLUVRUhGuuuQZvvPFGj+87OjpQVlaGAQMG4Pvf/z4++OADYef2q781NTW44YYbcO211zL30a/+Njc344orrsDNN9+MwsJClJeX49lnn5W2vxMmTMDvf/977NmzBwDw4Ycf4u2338b111/veX+bm5sxfvx41NTU4MILL8TIkSOxZMkSdHZ2Auiewba2tmLy5MlJ+02ePBk7d+6Urr9Oz+1Xf2V531j6K+J9M8QVceURXV1dSnV1taXU/ctf/qI0NjYqra2tys6dO5U777xTCYVCSbOCP/zhD8p//Md/KOFwWGlpaVGmTJminHPOOcqePXscndvP/m7cuFEZOXKk8n//93+KoijcMyiv+5udna1kZ2crdXV1yvvvv688/fTTSk5OjvKrX/1Kyv52dXUpCxYsUEKhkNKrVy8lFAopS5YsYeqr6P4OHTpUyc7OVm677TZl165dysaNG5X8/Hylvr5eURRFOXTokAJAeeedd5KOvXjxYuWSSy6Rrr92z+1nf2V631j66/R9MyPQguWuu+5SysrKlIMHD3Lv+/3vf1+prq42/L6zs1MZM2aMMmfOHGHn9rK/bW1tSmFhoRIOhxNteB90r+9v7969lfHjxye1mzNnjjJu3Dgp+7tx40ZlwIABysaNG5WPPvpIWbdunZKfn680NTV53t8hQ4YoJSUlypkzZxLbHnvsMaV///6KopwVLDt37kw6zsMPP6wMHTpUuv6KOLeX/ZXtfWO5v07fNzMCuxQ2Z84cNDc344033rAsOqPHuHHjsHfvXsPvMzIyMHbsWN02ds7tdX9bW1tx9OhRVFRUoFevXujVqxfefPNNrFixAr169TJdcvCjvwBQVFSEESNGJLUbPnw42traLM/nR39/9rOfYcGCBZg2bRpGjRqFW2+9Fffeey8aGho8729RUREuueQSZGZmJrYNHz4cR44cwenTp3HBBRcgMzMTR44cSTrO0aNHceGFF0rXX6fn9rq/sr1vLPfXyftmReAEi6IouPvuu/Gb3/wGr7/+OgYNGmTrOB988AGKiopMzxMOh5Pa2Dm3X/295ppr8PHHHyMcDif+rrjiCsyYMQPhcDjpgZOhvwBQWVnZwwtrz549KCsrMz2OX/39+uuvexQ8yszMNHU3dqu/lZWV2LdvX9K59+zZg6KiImRlZSErKwsVFRV47bXXko7z2muv4aqrrpKuv3bP7Vd/ZXvfWO6vnfeNGcc6j8fceeedSl5enrJjxw4lEokk/r7++utEmwULFii33npr4vOyZcuULVu2KHv27FH+/Oc/KwsWLFAAKL/+9a8TbR544AHl1VdfVT777DPlgw8+UGbNmqX06tVL+eMf/8h1bpn6q4VFNfezv3/605+UXr16KYsXL1b27t2rrF+/Xjn33HOV559/Xsr+zpw5U7nooouUV155Rdm/f7/ym9/8RrnggguU//f//p/n/W1ra1POO+885e6771Z2796tvPLKK0phYaHy8MMPJ9ps2rRJ6d27t/Lcc88pn3zyiVJbW6v06dNHOXDggJT9lel9Y+mvFj/fN5b+2nnfWAmcYAGg+7d27dpEm5kzZyrf/e53E5+XLl2qXHzxxUpOTo7yd3/3d8qECROU//qv/0o6bm1trVJaWqpkZWUpBQUFyuTJk3usR7OcW6b+amF50P3u78svv6yMHDlSyc7OVoYNG6Y0NjZK299oNKrMnTtXKS0tVXJycpTBgwcrCxcuVGKxmOf9VRRF2blzp/IP//APSnZ2tjJ48GBl8eLFSWvsiqIoTz75pFJWVqZkZWUpl19+uaXrrp/9lel9Y+mvFj/fN9b+8r5vrFDafIIgCEIogbOxEARBEHJDgoUgCIIQCgkWgiAIQigkWAiCIAihkGAhCIIghEKChSAIghAKCRaCIAhCKCRYCIIgCKGQYCEIgkgRWlpaUF1djeLiYoRCIWzdupX7GIqi4NFHH8Ull1yC7OxslJSUYMmSJVzHCFxpYoIgCEKfkydPYsyYMZg1axamTJli6xhz587F9u3b8eijj2LUqFFob2/H8ePHuY5BKV0IgiBSkFAohC1btuCmm25KbDt9+jTuu+8+rF+/HidOnMDIkSOxdOlSTJw4EQDw6aefYvTo0fjzn/+MoUOH2j43LYURBEGkCbNmzcI777yDTZs24aOPPsLNN9+M733ve4laLi+//DIGDx6MV155BYMGDcLAgQNx++2348svv+Q6DwkWgiCINOCzzz7Dxo0b8cILL+Dqq6/GxRdfjJ/+9KeYMGEC1q5dCwD4/PPP8cUXX+CFF17AunXr0NTUhNbWVvzwhz/kOhfZWAiCINKA999/H4qi4JJLLknaHovF0K9fPwBAV1cXYrEY1q1bl2j33HPPoaKiArt372ZeHiPBQhAEkQZ0dXUhMzMTra2tPSpannfeeQC6yxX36tUrSfgMHz4cANDW1kaChSAIgjhLeXk5Ojs7cfToUVx99dW6bSorK3HmzBl89tlnuPjiiwF0lysGwFWymLzCCIIgUoSOjg7s27cPQLcgefzxxzFp0iTk5+ejtLQUt9xyC9555x089thjKC8vx/Hjx/H6669j1KhRuP7669HV1YWxY8fivPPOwxNPPIGuri7U1NQgNzcX27dvZ+4HCRaCIIgUYceOHZg0aVKP7TNnzkRTUxO++eYbPPzww1i3bh0OHTqEfv36Yfz48aivr8eoUaMAAIcPH8acOXOwfft29OnTB1VVVXjssceQn5/P3A8SLARBEIRQyN2YIAiCEAoJFoIgCEIoJFgIgiAIoZBgIQiCIIRCgoUgCIIQCgkWgiAIQigkWAiCIAihkGAhCIIghEKChSAIghAKCRaCIAhCKCRYCIIgCKH8f1hOUc6ADMbSAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ls = pls.Landscape(input_filepath)\n",
    "ls.plot_landscape(cmap=sls.noas04_4_cmap, norm=sls.noas04_4_norm, legend=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a4fdfe55",
   "metadata": {},
   "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>class_val</th>\n",
       "      <th>area</th>\n",
       "      <th>perimeter</th>\n",
       "      <th>perimeter_area_ratio</th>\n",
       "      <th>shape_index</th>\n",
       "      <th>fractal_dimension</th>\n",
       "      <th>core_area</th>\n",
       "      <th>number_of_core_areas</th>\n",
       "      <th>core_area_index</th>\n",
       "      <th>euclidean_nearest_neighbor</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>patch_id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>400.0</td>\n",
       "      <td>400.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>360.555128</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>400.0</td>\n",
       "      <td>400.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>360.555128</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>400.0</td>\n",
       "      <td>400.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>200.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
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       "      <td>0.0</td>\n",
       "      <td>200.000000</td>\n",
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       "    <tr>\n",
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       "      <td>400.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>424.264069</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          class_val  area  perimeter  perimeter_area_ratio  shape_index  \\\n",
       "patch_id                                                                  \n",
       "0                 1   1.0      400.0                 400.0          1.0   \n",
       "1                 1   1.0      400.0                 400.0          1.0   \n",
       "2                 1   1.0      400.0                 400.0          1.0   \n",
       "3                 1   1.0      400.0                 400.0          1.0   \n",
       "4                 1   1.0      400.0                 400.0          1.0   \n",
       "\n",
       "          fractal_dimension  core_area  number_of_core_areas  core_area_index  \\\n",
       "patch_id                                                                        \n",
       "0                       1.0        0.0                     0              0.0   \n",
       "1                       1.0        0.0                     0              0.0   \n",
       "2                       1.0        0.0                     0              0.0   \n",
       "3                       1.0        0.0                     0              0.0   \n",
       "4                       1.0        0.0                     0              0.0   \n",
       "\n",
       "          euclidean_nearest_neighbor  \n",
       "patch_id                              \n",
       "0                         360.555128  \n",
       "1                         360.555128  \n",
       "2                         200.000000  \n",
       "3                         200.000000  \n",
       "4                         424.264069  "
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "patch_metrics_df = ls.compute_patch_metrics_df()\n",
    "patch_metrics_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1a0bebcf",
   "metadata": {},
   "outputs": [
    {
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       "      <td>287</td>\n",
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       "      <td>1.421872</td>\n",
       "      <td>256600.0</td>\n",
       "      <td>19.102211</td>\n",
       "      <td>90.0</td>\n",
       "      <td>0.669992</td>\n",
       "      <td>20</td>\n",
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       "      <td>223.606798</td>\n",
       "      <td>1100.000000</td>\n",
       "      <td>168.551251</td>\n",
       "      <td>54.504167</td>\n",
       "      <td>4.500000</td>\n",
       "      <td>20.400000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>8.458723</td>\n",
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       "      <td>58.862503</td>\n",
       "      <td>47</td>\n",
       "      <td>0.349885</td>\n",
       "      <td>46.862205</td>\n",
       "      <td>639500.0</td>\n",
       "      <td>47.606640</td>\n",
       "      <td>3589.0</td>\n",
       "      <td>26.717785</td>\n",
       "      <td>130</td>\n",
       "      <td>...</td>\n",
       "      <td>200.000000</td>\n",
       "      <td>561.577311</td>\n",
       "      <td>84.548722</td>\n",
       "      <td>36.703367</td>\n",
       "      <td>27.607692</td>\n",
       "      <td>747.952354</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1555.0</td>\n",
       "      <td>141.021466</td>\n",
       "      <td>510.804975</td>\n",
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       "      <th>3</th>\n",
       "      <td>4126.0</td>\n",
       "      <td>30.715402</td>\n",
       "      <td>165</td>\n",
       "      <td>1.228318</td>\n",
       "      <td>20.605970</td>\n",
       "      <td>434600.0</td>\n",
       "      <td>32.353160</td>\n",
       "      <td>1636.0</td>\n",
       "      <td>12.178962</td>\n",
       "      <td>75</td>\n",
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       "      <td>223.606798</td>\n",
       "      <td>528.010989</td>\n",
       "      <td>94.721867</td>\n",
       "      <td>35.526380</td>\n",
       "      <td>21.813333</td>\n",
       "      <td>198.039120</td>\n",
       "      <td>3.0</td>\n",
       "      <td>395.0</td>\n",
       "      <td>62.000579</td>\n",
       "      <td>284.232484</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>359.0</td>\n",
       "      <td>2.672523</td>\n",
       "      <td>137</td>\n",
       "      <td>1.019876</td>\n",
       "      <td>0.588104</td>\n",
       "      <td>75900.0</td>\n",
       "      <td>5.650264</td>\n",
       "      <td>58.0</td>\n",
       "      <td>0.431773</td>\n",
       "      <td>8</td>\n",
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       "      <td>412.310563</td>\n",
       "      <td>3124.154028</td>\n",
       "      <td>381.220569</td>\n",
       "      <td>74.637981</td>\n",
       "      <td>7.250000</td>\n",
       "      <td>12.896552</td>\n",
       "      <td>5.5</td>\n",
       "      <td>20.0</td>\n",
       "      <td>6.398242</td>\n",
       "      <td>88.251613</td>\n",
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      "text/plain": [
       "           total_area  proportion_of_landscape  number_of_patches  \\\n",
       "class_val                                                           \n",
       "1              1041.0                 7.749572                287   \n",
       "2              7907.0                58.862503                 47   \n",
       "3              4126.0                30.715402                165   \n",
       "4               359.0                 2.672523                137   \n",
       "\n",
       "           patch_density  largest_patch_index  total_edge  edge_density  \\\n",
       "class_val                                                                 \n",
       "1               2.136529             1.421872    256600.0     19.102211   \n",
       "2               0.349885            46.862205    639500.0     47.606640   \n",
       "3               1.228318            20.605970    434600.0     32.353160   \n",
       "4               1.019876             0.588104     75900.0      5.650264   \n",
       "\n",
       "           total_core_area  core_area_proportion_of_landscape  \\\n",
       "class_val                                                       \n",
       "1                     90.0                           0.669992   \n",
       "2                   3589.0                          26.717785   \n",
       "3                   1636.0                          12.178962   \n",
       "4                     58.0                           0.431773   \n",
       "\n",
       "           number_of_disjunct_core_areas  ...  euclidean_nearest_neighbor_md  \\\n",
       "class_val                                 ...                                  \n",
       "1                                     20  ...                     223.606798   \n",
       "2                                    130  ...                     200.000000   \n",
       "3                                     75  ...                     223.606798   \n",
       "4                                      8  ...                     412.310563   \n",
       "\n",
       "           euclidean_nearest_neighbor_ra  euclidean_nearest_neighbor_sd  \\\n",
       "class_val                                                                 \n",
       "1                            1100.000000                     168.551251   \n",
       "2                             561.577311                      84.548722   \n",
       "3                             528.010989                      94.721867   \n",
       "4                            3124.154028                     381.220569   \n",
       "\n",
       "           euclidean_nearest_neighbor_cv  disjunct_core_area_mn  \\\n",
       "class_val                                                         \n",
       "1                              54.504167               4.500000   \n",
       "2                              36.703367              27.607692   \n",
       "3                              35.526380              21.813333   \n",
       "4                              74.637981               7.250000   \n",
       "\n",
       "           disjunct_core_area_am  disjunct_core_area_md  \\\n",
       "class_val                                                 \n",
       "1                      20.400000                    1.0   \n",
       "2                     747.952354                    3.0   \n",
       "3                     198.039120                    3.0   \n",
       "4                      12.896552                    5.5   \n",
       "\n",
       "           disjunct_core_area_ra  disjunct_core_area_sd  disjunct_core_area_cv  \n",
       "class_val                                                                       \n",
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       "2                         1555.0             141.021466             510.804975  \n",
       "3                          395.0              62.000579             284.232484  \n",
       "4                           20.0               6.398242              88.251613  \n",
       "\n",
       "[4 rows x 66 columns]"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "class_metrics_df = ls.compute_class_metrics_df()\n",
    "class_metrics_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "94a0fd1c",
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "   total_area  number_of_patches  patch_density  largest_patch_index  \\\n",
       "0     13433.0                636       4.734609            46.862205   \n",
       "\n",
       "   total_edge  edge_density  total_core_area  number_of_disjunct_core_areas  \\\n",
       "0    703300.0     52.356138           5373.0                            233   \n",
       "\n",
       "   landscape_shape_index  effective_mesh_size  ...  \\\n",
       "0              17.278017          3593.476141  ...   \n",
       "\n",
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       "0              23.060086             509.698816                    3.0   \n",
       "\n",
       "   disjunct_core_area_ra  disjunct_core_area_sd  disjunct_core_area_cv  \n",
       "0                 1555.0             111.314332               482.7143  \n",
       "\n",
       "[1 rows x 71 columns]"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "landscape_metrics_df = ls.compute_landscape_metrics_df()\n",
    "landscape_metrics_df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c239cd6c",
   "metadata": {},
   "source": [
    "<a id='spatiotemporal-analysis'></a>\n",
    "## Spatio-temporal analysis\n",
    "\n",
    "Given a temporally-ordered sequence of landscape snapshots, we can also analyze the spatio-temporal patterns of landscape change. To that end, pylandstats can compute pandas dataframes with the evolution of the metrics and plot them, both at the class and landscape level. See the notebook [02-spatiotemporal-analysis.ipynb](https://github.com/martibosch/pylandstats-notebooks/blob/main/notebooks/02-spatiotemporal-analysis.ipynb) for a more thorough demonstration."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "02300607",
   "metadata": {},
   "outputs": [],
   "source": [
    "input_filepaths = [\n",
    "    \"../data/processed/veveyse/LU85_4.tif\",\n",
    "    \"../data/processed/veveyse/LU97_4.tif\",\n",
    "    \"../data/processed/veveyse/LU09_4.tif\",\n",
    "    \"../data/processed/veveyse/LU18_4.tif\",\n",
    "]\n",
    "years = [\"1980\", \"1992\", \"2004\", \"2013\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "328ccc85",
   "metadata": {},
   "outputs": [],
   "source": [
    "sta = pls.SpatioTemporalAnalysis(input_filepaths, dates=years)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "acb17f22",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[########################################] | 100% Completed | 1.29 ss\n"
     ]
    },
    {
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       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">1</th>\n",
       "      <th>1980</th>\n",
       "      <td>702.0</td>\n",
       "      <td>5.225936</td>\n",
       "      <td>304</td>\n",
       "      <td>2.263083</td>\n",
       "      <td>0.454106</td>\n",
       "      <td>208400.0</td>\n",
       "      <td>15.514033</td>\n",
       "      <td>25.0</td>\n",
       "      <td>0.186109</td>\n",
       "      <td>9</td>\n",
       "      <td>...</td>\n",
       "      <td>223.606798</td>\n",
       "      <td>1100.000000</td>\n",
       "      <td>169.300811</td>\n",
       "      <td>55.094159</td>\n",
       "      <td>2.777778</td>\n",
       "      <td>5.560000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>2.779999</td>\n",
       "      <td>100.079968</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1992</th>\n",
       "      <td>794.0</td>\n",
       "      <td>5.910817</td>\n",
       "      <td>309</td>\n",
       "      <td>2.300305</td>\n",
       "      <td>0.602993</td>\n",
       "      <td>227800.0</td>\n",
       "      <td>16.958237</td>\n",
       "      <td>28.0</td>\n",
       "      <td>0.208442</td>\n",
       "      <td>10</td>\n",
       "      <td>...</td>\n",
       "      <td>223.606798</td>\n",
       "      <td>940.175425</td>\n",
       "      <td>156.817097</td>\n",
       "      <td>52.299458</td>\n",
       "      <td>2.800000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>3.815757</td>\n",
       "      <td>136.277029</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2004</th>\n",
       "      <td>906.0</td>\n",
       "      <td>6.744584</td>\n",
       "      <td>304</td>\n",
       "      <td>2.263083</td>\n",
       "      <td>0.699769</td>\n",
       "      <td>242000.0</td>\n",
       "      <td>18.015335</td>\n",
       "      <td>50.0</td>\n",
       "      <td>0.372218</td>\n",
       "      <td>12</td>\n",
       "      <td>...</td>\n",
       "      <td>223.606798</td>\n",
       "      <td>1100.000000</td>\n",
       "      <td>162.962720</td>\n",
       "      <td>54.136494</td>\n",
       "      <td>4.166667</td>\n",
       "      <td>10.720000</td>\n",
       "      <td>1.5</td>\n",
       "      <td>16.0</td>\n",
       "      <td>5.225472</td>\n",
       "      <td>125.411323</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013</th>\n",
       "      <td>1041.0</td>\n",
       "      <td>7.749572</td>\n",
       "      <td>287</td>\n",
       "      <td>2.136529</td>\n",
       "      <td>1.421872</td>\n",
       "      <td>256600.0</td>\n",
       "      <td>19.102211</td>\n",
       "      <td>90.0</td>\n",
       "      <td>0.669992</td>\n",
       "      <td>20</td>\n",
       "      <td>...</td>\n",
       "      <td>223.606798</td>\n",
       "      <td>1100.000000</td>\n",
       "      <td>168.551251</td>\n",
       "      <td>54.504167</td>\n",
       "      <td>4.500000</td>\n",
       "      <td>20.400000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>8.458723</td>\n",
       "      <td>187.971629</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">2</th>\n",
       "      <th>1980</th>\n",
       "      <td>8351.0</td>\n",
       "      <td>62.167796</td>\n",
       "      <td>32</td>\n",
       "      <td>0.238219</td>\n",
       "      <td>52.170029</td>\n",
       "      <td>615000.0</td>\n",
       "      <td>45.782774</td>\n",
       "      <td>4076.0</td>\n",
       "      <td>30.343185</td>\n",
       "      <td>107</td>\n",
       "      <td>...</td>\n",
       "      <td>200.000000</td>\n",
       "      <td>470.820393</td>\n",
       "      <td>85.842455</td>\n",
       "      <td>36.252869</td>\n",
       "      <td>38.093458</td>\n",
       "      <td>740.707066</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1352.0</td>\n",
       "      <td>163.600067</td>\n",
       "      <td>429.470245</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1992</th>\n",
       "      <td>8185.0</td>\n",
       "      <td>60.932033</td>\n",
       "      <td>40</td>\n",
       "      <td>0.297774</td>\n",
       "      <td>48.596739</td>\n",
       "      <td>621700.0</td>\n",
       "      <td>46.281545</td>\n",
       "      <td>3902.0</td>\n",
       "      <td>29.047867</td>\n",
       "      <td>116</td>\n",
       "      <td>...</td>\n",
       "      <td>200.000000</td>\n",
       "      <td>561.577311</td>\n",
       "      <td>92.279333</td>\n",
       "      <td>38.656807</td>\n",
       "      <td>33.637931</td>\n",
       "      <td>754.402358</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1444.0</td>\n",
       "      <td>155.708137</td>\n",
       "      <td>462.894514</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2004</th>\n",
       "      <td>8052.0</td>\n",
       "      <td>59.941934</td>\n",
       "      <td>42</td>\n",
       "      <td>0.312663</td>\n",
       "      <td>47.829971</td>\n",
       "      <td>630500.0</td>\n",
       "      <td>46.936649</td>\n",
       "      <td>3735.0</td>\n",
       "      <td>27.804660</td>\n",
       "      <td>123</td>\n",
       "      <td>...</td>\n",
       "      <td>200.000000</td>\n",
       "      <td>561.577311</td>\n",
       "      <td>88.608659</td>\n",
       "      <td>37.780919</td>\n",
       "      <td>30.365854</td>\n",
       "      <td>1162.704953</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2049.0</td>\n",
       "      <td>185.430427</td>\n",
       "      <td>610.654419</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013</th>\n",
       "      <td>7907.0</td>\n",
       "      <td>58.862503</td>\n",
       "      <td>47</td>\n",
       "      <td>0.349885</td>\n",
       "      <td>46.862205</td>\n",
       "      <td>639500.0</td>\n",
       "      <td>47.606640</td>\n",
       "      <td>3589.0</td>\n",
       "      <td>26.717785</td>\n",
       "      <td>130</td>\n",
       "      <td>...</td>\n",
       "      <td>200.000000</td>\n",
       "      <td>561.577311</td>\n",
       "      <td>84.548722</td>\n",
       "      <td>36.703367</td>\n",
       "      <td>27.607692</td>\n",
       "      <td>747.952354</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1555.0</td>\n",
       "      <td>141.021466</td>\n",
       "      <td>510.804975</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">3</th>\n",
       "      <th>1980</th>\n",
       "      <td>3967.0</td>\n",
       "      <td>29.531750</td>\n",
       "      <td>169</td>\n",
       "      <td>1.258096</td>\n",
       "      <td>15.610809</td>\n",
       "      <td>438500.0</td>\n",
       "      <td>32.643490</td>\n",
       "      <td>1509.0</td>\n",
       "      <td>11.233529</td>\n",
       "      <td>76</td>\n",
       "      <td>...</td>\n",
       "      <td>223.606798</td>\n",
       "      <td>408.276253</td>\n",
       "      <td>89.161532</td>\n",
       "      <td>34.170355</td>\n",
       "      <td>19.855263</td>\n",
       "      <td>171.243870</td>\n",
       "      <td>4.5</td>\n",
       "      <td>360.0</td>\n",
       "      <td>54.825730</td>\n",
       "      <td>276.126935</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1992</th>\n",
       "      <td>4072.0</td>\n",
       "      <td>30.313407</td>\n",
       "      <td>161</td>\n",
       "      <td>1.198541</td>\n",
       "      <td>18.573662</td>\n",
       "      <td>439500.0</td>\n",
       "      <td>32.717933</td>\n",
       "      <td>1584.0</td>\n",
       "      <td>11.791856</td>\n",
       "      <td>76</td>\n",
       "      <td>...</td>\n",
       "      <td>223.606798</td>\n",
       "      <td>408.276253</td>\n",
       "      <td>89.394894</td>\n",
       "      <td>33.994777</td>\n",
       "      <td>20.842105</td>\n",
       "      <td>187.746212</td>\n",
       "      <td>3.5</td>\n",
       "      <td>386.0</td>\n",
       "      <td>58.979937</td>\n",
       "      <td>282.984546</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2004</th>\n",
       "      <td>4112.0</td>\n",
       "      <td>30.611181</td>\n",
       "      <td>162</td>\n",
       "      <td>1.205985</td>\n",
       "      <td>20.576193</td>\n",
       "      <td>434300.0</td>\n",
       "      <td>32.330827</td>\n",
       "      <td>1633.0</td>\n",
       "      <td>12.156629</td>\n",
       "      <td>76</td>\n",
       "      <td>...</td>\n",
       "      <td>223.606798</td>\n",
       "      <td>528.010989</td>\n",
       "      <td>95.923385</td>\n",
       "      <td>36.129383</td>\n",
       "      <td>21.486842</td>\n",
       "      <td>192.037355</td>\n",
       "      <td>3.0</td>\n",
       "      <td>395.0</td>\n",
       "      <td>60.535873</td>\n",
       "      <td>281.734621</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013</th>\n",
       "      <td>4126.0</td>\n",
       "      <td>30.715402</td>\n",
       "      <td>165</td>\n",
       "      <td>1.228318</td>\n",
       "      <td>20.605970</td>\n",
       "      <td>434600.0</td>\n",
       "      <td>32.353160</td>\n",
       "      <td>1636.0</td>\n",
       "      <td>12.178962</td>\n",
       "      <td>75</td>\n",
       "      <td>...</td>\n",
       "      <td>223.606798</td>\n",
       "      <td>528.010989</td>\n",
       "      <td>94.721867</td>\n",
       "      <td>35.526380</td>\n",
       "      <td>21.813333</td>\n",
       "      <td>198.039120</td>\n",
       "      <td>3.0</td>\n",
       "      <td>395.0</td>\n",
       "      <td>62.000579</td>\n",
       "      <td>284.232484</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">4</th>\n",
       "      <th>1980</th>\n",
       "      <td>413.0</td>\n",
       "      <td>3.074518</td>\n",
       "      <td>161</td>\n",
       "      <td>1.198541</td>\n",
       "      <td>0.580660</td>\n",
       "      <td>92100.0</td>\n",
       "      <td>6.856250</td>\n",
       "      <td>57.0</td>\n",
       "      <td>0.424328</td>\n",
       "      <td>8</td>\n",
       "      <td>...</td>\n",
       "      <td>360.555128</td>\n",
       "      <td>3124.154028</td>\n",
       "      <td>330.484033</td>\n",
       "      <td>74.180497</td>\n",
       "      <td>7.125000</td>\n",
       "      <td>12.403509</td>\n",
       "      <td>6.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>6.132648</td>\n",
       "      <td>86.072257</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1992</th>\n",
       "      <td>382.0</td>\n",
       "      <td>2.843743</td>\n",
       "      <td>152</td>\n",
       "      <td>1.131542</td>\n",
       "      <td>0.595548</td>\n",
       "      <td>83000.0</td>\n",
       "      <td>6.178813</td>\n",
       "      <td>58.0</td>\n",
       "      <td>0.431773</td>\n",
       "      <td>7</td>\n",
       "      <td>...</td>\n",
       "      <td>400.000000</td>\n",
       "      <td>3124.154028</td>\n",
       "      <td>352.800892</td>\n",
       "      <td>74.148800</td>\n",
       "      <td>8.285714</td>\n",
       "      <td>13.551724</td>\n",
       "      <td>9.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>6.605502</td>\n",
       "      <td>79.721573</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2004</th>\n",
       "      <td>363.0</td>\n",
       "      <td>2.702300</td>\n",
       "      <td>144</td>\n",
       "      <td>1.071987</td>\n",
       "      <td>0.580660</td>\n",
       "      <td>76000.0</td>\n",
       "      <td>5.657709</td>\n",
       "      <td>58.0</td>\n",
       "      <td>0.431773</td>\n",
       "      <td>8</td>\n",
       "      <td>...</td>\n",
       "      <td>412.310563</td>\n",
       "      <td>3124.154028</td>\n",
       "      <td>366.682284</td>\n",
       "      <td>72.359808</td>\n",
       "      <td>7.250000</td>\n",
       "      <td>12.896552</td>\n",
       "      <td>5.5</td>\n",
       "      <td>20.0</td>\n",
       "      <td>6.398242</td>\n",
       "      <td>88.251613</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013</th>\n",
       "      <td>359.0</td>\n",
       "      <td>2.672523</td>\n",
       "      <td>137</td>\n",
       "      <td>1.019876</td>\n",
       "      <td>0.588104</td>\n",
       "      <td>75900.0</td>\n",
       "      <td>5.650264</td>\n",
       "      <td>58.0</td>\n",
       "      <td>0.431773</td>\n",
       "      <td>8</td>\n",
       "      <td>...</td>\n",
       "      <td>412.310563</td>\n",
       "      <td>3124.154028</td>\n",
       "      <td>381.220569</td>\n",
       "      <td>74.637981</td>\n",
       "      <td>7.250000</td>\n",
       "      <td>12.896552</td>\n",
       "      <td>5.5</td>\n",
       "      <td>20.0</td>\n",
       "      <td>6.398242</td>\n",
       "      <td>88.251613</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>16 rows × 66 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 total_area  proportion_of_landscape  number_of_patches  \\\n",
       "class_val dates                                                           \n",
       "1         1980        702.0                 5.225936                304   \n",
       "          1992        794.0                 5.910817                309   \n",
       "          2004        906.0                 6.744584                304   \n",
       "          2013       1041.0                 7.749572                287   \n",
       "2         1980       8351.0                62.167796                 32   \n",
       "          1992       8185.0                60.932033                 40   \n",
       "          2004       8052.0                59.941934                 42   \n",
       "          2013       7907.0                58.862503                 47   \n",
       "3         1980       3967.0                29.531750                169   \n",
       "          1992       4072.0                30.313407                161   \n",
       "          2004       4112.0                30.611181                162   \n",
       "          2013       4126.0                30.715402                165   \n",
       "4         1980        413.0                 3.074518                161   \n",
       "          1992        382.0                 2.843743                152   \n",
       "          2004        363.0                 2.702300                144   \n",
       "          2013        359.0                 2.672523                137   \n",
       "\n",
       "                 patch_density  largest_patch_index  total_edge  edge_density  \\\n",
       "class_val dates                                                                 \n",
       "1         1980        2.263083             0.454106    208400.0     15.514033   \n",
       "          1992        2.300305             0.602993    227800.0     16.958237   \n",
       "          2004        2.263083             0.699769    242000.0     18.015335   \n",
       "          2013        2.136529             1.421872    256600.0     19.102211   \n",
       "2         1980        0.238219            52.170029    615000.0     45.782774   \n",
       "          1992        0.297774            48.596739    621700.0     46.281545   \n",
       "          2004        0.312663            47.829971    630500.0     46.936649   \n",
       "          2013        0.349885            46.862205    639500.0     47.606640   \n",
       "3         1980        1.258096            15.610809    438500.0     32.643490   \n",
       "          1992        1.198541            18.573662    439500.0     32.717933   \n",
       "          2004        1.205985            20.576193    434300.0     32.330827   \n",
       "          2013        1.228318            20.605970    434600.0     32.353160   \n",
       "4         1980        1.198541             0.580660     92100.0      6.856250   \n",
       "          1992        1.131542             0.595548     83000.0      6.178813   \n",
       "          2004        1.071987             0.580660     76000.0      5.657709   \n",
       "          2013        1.019876             0.588104     75900.0      5.650264   \n",
       "\n",
       "                 total_core_area  core_area_proportion_of_landscape  \\\n",
       "class_val dates                                                       \n",
       "1         1980              25.0                           0.186109   \n",
       "          1992              28.0                           0.208442   \n",
       "          2004              50.0                           0.372218   \n",
       "          2013              90.0                           0.669992   \n",
       "2         1980            4076.0                          30.343185   \n",
       "          1992            3902.0                          29.047867   \n",
       "          2004            3735.0                          27.804660   \n",
       "          2013            3589.0                          26.717785   \n",
       "3         1980            1509.0                          11.233529   \n",
       "          1992            1584.0                          11.791856   \n",
       "          2004            1633.0                          12.156629   \n",
       "          2013            1636.0                          12.178962   \n",
       "4         1980              57.0                           0.424328   \n",
       "          1992              58.0                           0.431773   \n",
       "          2004              58.0                           0.431773   \n",
       "          2013              58.0                           0.431773   \n",
       "\n",
       "                 number_of_disjunct_core_areas  ...  \\\n",
       "class_val dates                                 ...   \n",
       "1         1980                               9  ...   \n",
       "          1992                              10  ...   \n",
       "          2004                              12  ...   \n",
       "          2013                              20  ...   \n",
       "2         1980                             107  ...   \n",
       "          1992                             116  ...   \n",
       "          2004                             123  ...   \n",
       "          2013                             130  ...   \n",
       "3         1980                              76  ...   \n",
       "          1992                              76  ...   \n",
       "          2004                              76  ...   \n",
       "          2013                              75  ...   \n",
       "4         1980                               8  ...   \n",
       "          1992                               7  ...   \n",
       "          2004                               8  ...   \n",
       "          2013                               8  ...   \n",
       "\n",
       "                 euclidean_nearest_neighbor_md  euclidean_nearest_neighbor_ra  \\\n",
       "class_val dates                                                                 \n",
       "1         1980                      223.606798                    1100.000000   \n",
       "          1992                      223.606798                     940.175425   \n",
       "          2004                      223.606798                    1100.000000   \n",
       "          2013                      223.606798                    1100.000000   \n",
       "2         1980                      200.000000                     470.820393   \n",
       "          1992                      200.000000                     561.577311   \n",
       "          2004                      200.000000                     561.577311   \n",
       "          2013                      200.000000                     561.577311   \n",
       "3         1980                      223.606798                     408.276253   \n",
       "          1992                      223.606798                     408.276253   \n",
       "          2004                      223.606798                     528.010989   \n",
       "          2013                      223.606798                     528.010989   \n",
       "4         1980                      360.555128                    3124.154028   \n",
       "          1992                      400.000000                    3124.154028   \n",
       "          2004                      412.310563                    3124.154028   \n",
       "          2013                      412.310563                    3124.154028   \n",
       "\n",
       "                 euclidean_nearest_neighbor_sd  euclidean_nearest_neighbor_cv  \\\n",
       "class_val dates                                                                 \n",
       "1         1980                      169.300811                      55.094159   \n",
       "          1992                      156.817097                      52.299458   \n",
       "          2004                      162.962720                      54.136494   \n",
       "          2013                      168.551251                      54.504167   \n",
       "2         1980                       85.842455                      36.252869   \n",
       "          1992                       92.279333                      38.656807   \n",
       "          2004                       88.608659                      37.780919   \n",
       "          2013                       84.548722                      36.703367   \n",
       "3         1980                       89.161532                      34.170355   \n",
       "          1992                       89.394894                      33.994777   \n",
       "          2004                       95.923385                      36.129383   \n",
       "          2013                       94.721867                      35.526380   \n",
       "4         1980                      330.484033                      74.180497   \n",
       "          1992                      352.800892                      74.148800   \n",
       "          2004                      366.682284                      72.359808   \n",
       "          2013                      381.220569                      74.637981   \n",
       "\n",
       "                 disjunct_core_area_mn  disjunct_core_area_am  \\\n",
       "class_val dates                                                 \n",
       "1         1980                2.777778               5.560000   \n",
       "          1992                2.800000               8.000000   \n",
       "          2004                4.166667              10.720000   \n",
       "          2013                4.500000              20.400000   \n",
       "2         1980               38.093458             740.707066   \n",
       "          1992               33.637931             754.402358   \n",
       "          2004               30.365854            1162.704953   \n",
       "          2013               27.607692             747.952354   \n",
       "3         1980               19.855263             171.243870   \n",
       "          1992               20.842105             187.746212   \n",
       "          2004               21.486842             192.037355   \n",
       "          2013               21.813333             198.039120   \n",
       "4         1980                7.125000              12.403509   \n",
       "          1992                8.285714              13.551724   \n",
       "          2004                7.250000              12.896552   \n",
       "          2013                7.250000              12.896552   \n",
       "\n",
       "                 disjunct_core_area_md  disjunct_core_area_ra  \\\n",
       "class_val dates                                                 \n",
       "1         1980                     1.0                    9.0   \n",
       "          1992                     1.0                   13.0   \n",
       "          2004                     1.5                   16.0   \n",
       "          2013                     1.0                   31.0   \n",
       "2         1980                     3.0                 1352.0   \n",
       "          1992                     3.0                 1444.0   \n",
       "          2004                     3.0                 2049.0   \n",
       "          2013                     3.0                 1555.0   \n",
       "3         1980                     4.5                  360.0   \n",
       "          1992                     3.5                  386.0   \n",
       "          2004                     3.0                  395.0   \n",
       "          2013                     3.0                  395.0   \n",
       "4         1980                     6.0                   18.0   \n",
       "          1992                     9.0                   18.0   \n",
       "          2004                     5.5                   20.0   \n",
       "          2013                     5.5                   20.0   \n",
       "\n",
       "                 disjunct_core_area_sd  disjunct_core_area_cv  \n",
       "class_val dates                                                \n",
       "1         1980                2.779999             100.079968  \n",
       "          1992                3.815757             136.277029  \n",
       "          2004                5.225472             125.411323  \n",
       "          2013                8.458723             187.971629  \n",
       "2         1980              163.600067             429.470245  \n",
       "          1992              155.708137             462.894514  \n",
       "          2004              185.430427             610.654419  \n",
       "          2013              141.021466             510.804975  \n",
       "3         1980               54.825730             276.126935  \n",
       "          1992               58.979937             282.984546  \n",
       "          2004               60.535873             281.734621  \n",
       "          2013               62.000579             284.232484  \n",
       "4         1980                6.132648              86.072257  \n",
       "          1992                6.605502              79.721573  \n",
       "          2004                6.398242              88.251613  \n",
       "          2013                6.398242              88.251613  \n",
       "\n",
       "[16 rows x 66 columns]"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sta.compute_class_metrics_df()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4fa95cc7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[########################################] | 100% Completed | 3.37 ss\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>total_area</th>\n",
       "      <th>number_of_patches</th>\n",
       "      <th>patch_density</th>\n",
       "      <th>largest_patch_index</th>\n",
       "      <th>total_edge</th>\n",
       "      <th>edge_density</th>\n",
       "      <th>total_core_area</th>\n",
       "      <th>number_of_disjunct_core_areas</th>\n",
       "      <th>landscape_shape_index</th>\n",
       "      <th>effective_mesh_size</th>\n",
       "      <th>...</th>\n",
       "      <th>euclidean_nearest_neighbor_md</th>\n",
       "      <th>euclidean_nearest_neighbor_ra</th>\n",
       "      <th>euclidean_nearest_neighbor_sd</th>\n",
       "      <th>euclidean_nearest_neighbor_cv</th>\n",
       "      <th>disjunct_core_area_mn</th>\n",
       "      <th>disjunct_core_area_am</th>\n",
       "      <th>disjunct_core_area_md</th>\n",
       "      <th>disjunct_core_area_ra</th>\n",
       "      <th>disjunct_core_area_sd</th>\n",
       "      <th>disjunct_core_area_cv</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dates</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1980</th>\n",
       "      <td>13433.0</td>\n",
       "      <td>666</td>\n",
       "      <td>4.957939</td>\n",
       "      <td>52.170029</td>\n",
       "      <td>677000.0</td>\n",
       "      <td>50.398273</td>\n",
       "      <td>5667.0</td>\n",
       "      <td>200</td>\n",
       "      <td>16.711207</td>\n",
       "      <td>4074.760143</td>\n",
       "      <td>...</td>\n",
       "      <td>223.606798</td>\n",
       "      <td>3124.154028</td>\n",
       "      <td>216.625926</td>\n",
       "      <td>66.540480</td>\n",
       "      <td>28.335000</td>\n",
       "      <td>533.272195</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1352.0</td>\n",
       "      <td>124.855928</td>\n",
       "      <td>440.642061</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1992</th>\n",
       "      <td>13433.0</td>\n",
       "      <td>662</td>\n",
       "      <td>4.928162</td>\n",
       "      <td>48.596739</td>\n",
       "      <td>686000.0</td>\n",
       "      <td>51.068265</td>\n",
       "      <td>5572.0</td>\n",
       "      <td>209</td>\n",
       "      <td>16.905172</td>\n",
       "      <td>3721.028140</td>\n",
       "      <td>...</td>\n",
       "      <td>223.606798</td>\n",
       "      <td>3124.154028</td>\n",
       "      <td>222.286812</td>\n",
       "      <td>67.856637</td>\n",
       "      <td>26.660287</td>\n",
       "      <td>534.324217</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1444.0</td>\n",
       "      <td>121.661679</td>\n",
       "      <td>456.340467</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2004</th>\n",
       "      <td>13433.0</td>\n",
       "      <td>652</td>\n",
       "      <td>4.853718</td>\n",
       "      <td>47.829971</td>\n",
       "      <td>691400.0</td>\n",
       "      <td>51.470260</td>\n",
       "      <td>5476.0</td>\n",
       "      <td>219</td>\n",
       "      <td>17.021552</td>\n",
       "      <td>3711.910593</td>\n",
       "      <td>...</td>\n",
       "      <td>223.606798</td>\n",
       "      <td>3124.154028</td>\n",
       "      <td>231.885409</td>\n",
       "      <td>69.562278</td>\n",
       "      <td>25.004566</td>\n",
       "      <td>777.260594</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2049.0</td>\n",
       "      <td>143.674210</td>\n",
       "      <td>574.591890</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013</th>\n",
       "      <td>13433.0</td>\n",
       "      <td>636</td>\n",
       "      <td>4.734609</td>\n",
       "      <td>46.862205</td>\n",
       "      <td>703300.0</td>\n",
       "      <td>52.356138</td>\n",
       "      <td>5373.0</td>\n",
       "      <td>233</td>\n",
       "      <td>17.278017</td>\n",
       "      <td>3593.476141</td>\n",
       "      <td>...</td>\n",
       "      <td>223.606798</td>\n",
       "      <td>3124.154028</td>\n",
       "      <td>236.550901</td>\n",
       "      <td>70.451170</td>\n",
       "      <td>23.060086</td>\n",
       "      <td>509.698816</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1555.0</td>\n",
       "      <td>111.314332</td>\n",
       "      <td>482.714300</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4 rows × 71 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       total_area  number_of_patches  patch_density  largest_patch_index  \\\n",
       "dates                                                                      \n",
       "1980      13433.0                666       4.957939            52.170029   \n",
       "1992      13433.0                662       4.928162            48.596739   \n",
       "2004      13433.0                652       4.853718            47.829971   \n",
       "2013      13433.0                636       4.734609            46.862205   \n",
       "\n",
       "       total_edge  edge_density  total_core_area  \\\n",
       "dates                                              \n",
       "1980     677000.0     50.398273           5667.0   \n",
       "1992     686000.0     51.068265           5572.0   \n",
       "2004     691400.0     51.470260           5476.0   \n",
       "2013     703300.0     52.356138           5373.0   \n",
       "\n",
       "       number_of_disjunct_core_areas  landscape_shape_index  \\\n",
       "dates                                                         \n",
       "1980                             200              16.711207   \n",
       "1992                             209              16.905172   \n",
       "2004                             219              17.021552   \n",
       "2013                             233              17.278017   \n",
       "\n",
       "       effective_mesh_size  ...  euclidean_nearest_neighbor_md  \\\n",
       "dates                       ...                                  \n",
       "1980           4074.760143  ...                     223.606798   \n",
       "1992           3721.028140  ...                     223.606798   \n",
       "2004           3711.910593  ...                     223.606798   \n",
       "2013           3593.476141  ...                     223.606798   \n",
       "\n",
       "       euclidean_nearest_neighbor_ra  euclidean_nearest_neighbor_sd  \\\n",
       "dates                                                                 \n",
       "1980                     3124.154028                     216.625926   \n",
       "1992                     3124.154028                     222.286812   \n",
       "2004                     3124.154028                     231.885409   \n",
       "2013                     3124.154028                     236.550901   \n",
       "\n",
       "       euclidean_nearest_neighbor_cv  disjunct_core_area_mn  \\\n",
       "dates                                                         \n",
       "1980                       66.540480              28.335000   \n",
       "1992                       67.856637              26.660287   \n",
       "2004                       69.562278              25.004566   \n",
       "2013                       70.451170              23.060086   \n",
       "\n",
       "       disjunct_core_area_am  disjunct_core_area_md  disjunct_core_area_ra  \\\n",
       "dates                                                                        \n",
       "1980              533.272195                    3.0                 1352.0   \n",
       "1992              534.324217                    3.0                 1444.0   \n",
       "2004              777.260594                    3.0                 2049.0   \n",
       "2013              509.698816                    3.0                 1555.0   \n",
       "\n",
       "       disjunct_core_area_sd  disjunct_core_area_cv  \n",
       "dates                                                \n",
       "1980              124.855928             440.642061  \n",
       "1992              121.661679             456.340467  \n",
       "2004              143.674210             574.591890  \n",
       "2013              111.314332             482.714300  \n",
       "\n",
       "[4 rows x 71 columns]"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sta.compute_landscape_metrics_df()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e14fd377",
   "metadata": {},
   "source": [
    "We can also plot the time series of metrics at the class level, e.g., the evolution of the proportion of landscape occupied by the land use class value `1` (urban):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3931db12",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[########################################] | 100% Completed | 101.20 ms\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Axes: ylabel='PLAND'>"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sta.plot_metric(\"proportion_of_landscape\", class_val=URBAN_CLASS_VAL)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8b40c32b",
   "metadata": {},
   "source": [
    "or we can also plot at the landscape level by not providing any `class_val` argument, e.g., the evolution of the area-weighted mean fractal dimension of all the patches of the landscape:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cc0ed365",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[########################################] | 100% Completed | 107.28 ms\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Axes: ylabel='FRAC_AM'>"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sta.plot_metric(\"fractal_dimension_am\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "017f6448",
   "metadata": {},
   "source": [
    "<a id='zonal-analysis'></a>\n",
    "## Zonal analysis\n",
    "\n",
    "Zonal analysis is a common procedure to compute statistics for a set of specified spatial zones. PyLandStats features three classes to perform zonal analysis, `ZonalAnalysis`, `BufferAnalysis` and `ZonalGridAnalysis`. The first allows user to fully customize how the zones are defined, while `BufferAnalysis` and `ZonalGriAnalysis` provide a convenient way to instantiate specific cases of zonal analysis, i.e., adding buffers around a feature of interest or as a regular rectangular grid over the landscape, respectively. See the notebook [03-zonal-analysis.ipynb](https://github.com/martibosch/pylandstats-notebooks/blob/main/notebooks/03-zonal-analysis.ipynb) for a thorough demonstration of the use cases described above.\n",
    "\n",
    "To define the zones of a `ZonalAnalysis`, we can use - among other options - any geographic data file that can be read by [geopandas.read_file](https://geopandas.org/en/stable/docs/reference/api/geopandas.read_file.html#geopandas.read_file). For instance, we can use a geopackage file defining three elevation zones in our landscape:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "092b32eb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1920x480 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "elev_zones_filepath = \"../data/processed/elev-zones.gpkg\"\n",
    "\n",
    "za = pls.ZonalAnalysis(input_filepath, elev_zones_filepath, zone_index=\"elev-zone\")\n",
    "# plot the landscapes of each zone\n",
    "fig = za.plot_landscapes(\n",
    "    cmap=sls.noas04_4_cmap, show_kwargs=dict(norm=sls.noas04_4_norm)\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "db49c2e9",
   "metadata": {},
   "source": [
    "Analogously to the spatio-temporal analysis, we can use the `compute_class_metrics_df` and `compute_landscape_metrics_df` methods to compute the metrics for each zone:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1cc74c48",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[########################################] | 100% Completed | 1.71 ss\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/martibosch/mambaforge/envs/pylandstats/lib/python3.11/site-packages/numpy/core/fromnumeric.py:3504: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/martibosch/mambaforge/envs/pylandstats/lib/python3.11/site-packages/numpy/core/_methods.py:129: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  ret = ret.dtype.type(ret / rcount)\n",
      "/home/martibosch/mambaforge/envs/pylandstats/lib/python3.11/site-packages/numpy/core/fromnumeric.py:3504: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/martibosch/mambaforge/envs/pylandstats/lib/python3.11/site-packages/numpy/core/_methods.py:129: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  ret = ret.dtype.type(ret / rcount)\n"
     ]
    },
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>total_area</th>\n",
       "      <th>proportion_of_landscape</th>\n",
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       "      <td>1.054852</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>2202.271555</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">2</th>\n",
       "      <th>&lt;1000</th>\n",
       "      <td>5753.0</td>\n",
       "      <td>70.806154</td>\n",
       "      <td>26</td>\n",
       "      <td>0.320000</td>\n",
       "      <td>67.876923</td>\n",
       "      <td>422100.0</td>\n",
       "      <td>51.950769</td>\n",
       "      <td>2722.0</td>\n",
       "      <td>33.501538</td>\n",
       "      <td>81</td>\n",
       "      <td>...</td>\n",
       "      <td>200.000000</td>\n",
       "      <td>694.427191</td>\n",
       "      <td>135.571676</td>\n",
       "      <td>55.309188</td>\n",
       "      <td>33.604938</td>\n",
       "      <td>935.440852</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1525.0</td>\n",
       "      <td>174.086588</td>\n",
       "      <td>518.038709</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1000-1500</th>\n",
       "      <td>1800.0</td>\n",
       "      <td>39.447732</td>\n",
       "      <td>45</td>\n",
       "      <td>0.986193</td>\n",
       "      <td>12.447951</td>\n",
       "      <td>178900.0</td>\n",
       "      <td>39.206662</td>\n",
       "      <td>618.0</td>\n",
       "      <td>13.543721</td>\n",
       "      <td>56</td>\n",
       "      <td>...</td>\n",
       "      <td>200.000000</td>\n",
       "      <td>561.577311</td>\n",
       "      <td>87.023996</td>\n",
       "      <td>37.075659</td>\n",
       "      <td>11.035714</td>\n",
       "      <td>46.148867</td>\n",
       "      <td>3.5</td>\n",
       "      <td>128.0</td>\n",
       "      <td>19.684987</td>\n",
       "      <td>178.375289</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>&gt;1500</th>\n",
       "      <td>237.0</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>11</td>\n",
       "      <td>2.320675</td>\n",
       "      <td>17.932489</td>\n",
       "      <td>15900.0</td>\n",
       "      <td>33.544304</td>\n",
       "      <td>48.0</td>\n",
       "      <td>10.126582</td>\n",
       "      <td>6</td>\n",
       "      <td>...</td>\n",
       "      <td>200.000000</td>\n",
       "      <td>300.000000</td>\n",
       "      <td>105.269873</td>\n",
       "      <td>36.638140</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>12.500000</td>\n",
       "      <td>8.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>75.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">3</th>\n",
       "      <th>&lt;1000</th>\n",
       "      <td>1339.0</td>\n",
       "      <td>16.480000</td>\n",
       "      <td>151</td>\n",
       "      <td>1.858462</td>\n",
       "      <td>2.535385</td>\n",
       "      <td>213500.0</td>\n",
       "      <td>26.276923</td>\n",
       "      <td>212.0</td>\n",
       "      <td>2.609231</td>\n",
       "      <td>34</td>\n",
       "      <td>...</td>\n",
       "      <td>223.606798</td>\n",
       "      <td>528.010989</td>\n",
       "      <td>95.872336</td>\n",
       "      <td>34.919733</td>\n",
       "      <td>6.235294</td>\n",
       "      <td>24.688679</td>\n",
       "      <td>3.0</td>\n",
       "      <td>62.0</td>\n",
       "      <td>10.726709</td>\n",
       "      <td>172.032124</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1000-1500</th>\n",
       "      <td>2583.0</td>\n",
       "      <td>56.607495</td>\n",
       "      <td>41</td>\n",
       "      <td>0.898532</td>\n",
       "      <td>46.833224</td>\n",
       "      <td>188800.0</td>\n",
       "      <td>41.376288</td>\n",
       "      <td>1283.0</td>\n",
       "      <td>28.117467</td>\n",
       "      <td>37</td>\n",
       "      <td>...</td>\n",
       "      <td>223.606798</td>\n",
       "      <td>160.555128</td>\n",
       "      <td>44.752789</td>\n",
       "      <td>18.862749</td>\n",
       "      <td>34.675676</td>\n",
       "      <td>226.632112</td>\n",
       "      <td>4.0</td>\n",
       "      <td>387.0</td>\n",
       "      <td>81.585655</td>\n",
       "      <td>235.282092</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>&gt;1500</th>\n",
       "      <td>78.0</td>\n",
       "      <td>16.455696</td>\n",
       "      <td>15</td>\n",
       "      <td>3.164557</td>\n",
       "      <td>8.016878</td>\n",
       "      <td>9300.0</td>\n",
       "      <td>19.620253</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.265823</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>282.842712</td>\n",
       "      <td>1052.996409</td>\n",
       "      <td>331.304373</td>\n",
       "      <td>76.908158</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>4.333333</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>66.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">4</th>\n",
       "      <th>&lt;1000</th>\n",
       "      <td>122.0</td>\n",
       "      <td>1.501538</td>\n",
       "      <td>79</td>\n",
       "      <td>0.972308</td>\n",
       "      <td>0.283077</td>\n",
       "      <td>37800.0</td>\n",
       "      <td>4.652308</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.049231</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>447.213595</td>\n",
       "      <td>1624.828759</td>\n",
       "      <td>318.697963</td>\n",
       "      <td>58.713471</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1000-1500</th>\n",
       "      <td>63.0</td>\n",
       "      <td>1.380671</td>\n",
       "      <td>43</td>\n",
       "      <td>0.942362</td>\n",
       "      <td>0.065746</td>\n",
       "      <td>21100.0</td>\n",
       "      <td>4.624151</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>447.213595</td>\n",
       "      <td>1569.180601</td>\n",
       "      <td>354.559752</td>\n",
       "      <td>66.120768</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>&gt;1500</th>\n",
       "      <td>157.0</td>\n",
       "      <td>33.122363</td>\n",
       "      <td>11</td>\n",
       "      <td>2.320675</td>\n",
       "      <td>15.611814</td>\n",
       "      <td>12500.0</td>\n",
       "      <td>26.371308</td>\n",
       "      <td>44.0</td>\n",
       "      <td>9.282700</td>\n",
       "      <td>6</td>\n",
       "      <td>...</td>\n",
       "      <td>223.606798</td>\n",
       "      <td>247.213595</td>\n",
       "      <td>74.100090</td>\n",
       "      <td>29.382577</td>\n",
       "      <td>7.333333</td>\n",
       "      <td>13.181818</td>\n",
       "      <td>4.5</td>\n",
       "      <td>20.0</td>\n",
       "      <td>6.548961</td>\n",
       "      <td>89.304012</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>12 rows × 66 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                     total_area  proportion_of_landscape  number_of_patches  \\\n",
       "class_val elev-zone                                                           \n",
       "1         <1000           911.0                11.212308                232   \n",
       "          1000-1500       117.0                 2.564103                 49   \n",
       "          >1500             2.0                 0.421941                  2   \n",
       "2         <1000          5753.0                70.806154                 26   \n",
       "          1000-1500      1800.0                39.447732                 45   \n",
       "          >1500           237.0                50.000000                 11   \n",
       "3         <1000          1339.0                16.480000                151   \n",
       "          1000-1500      2583.0                56.607495                 41   \n",
       "          >1500            78.0                16.455696                 15   \n",
       "4         <1000           122.0                 1.501538                 79   \n",
       "          1000-1500        63.0                 1.380671                 43   \n",
       "          >1500           157.0                33.122363                 11   \n",
       "\n",
       "                     patch_density  largest_patch_index  total_edge  \\\n",
       "class_val elev-zone                                                   \n",
       "1         <1000           2.855385             2.289231    219200.0   \n",
       "          1000-1500       1.073855             0.460224     32200.0   \n",
       "          >1500           0.421941             0.210970       500.0   \n",
       "2         <1000           0.320000            67.876923    422100.0   \n",
       "          1000-1500       0.986193            12.447951    178900.0   \n",
       "          >1500           2.320675            17.932489     15900.0   \n",
       "3         <1000           1.858462             2.535385    213500.0   \n",
       "          1000-1500       0.898532            46.833224    188800.0   \n",
       "          >1500           3.164557             8.016878      9300.0   \n",
       "4         <1000           0.972308             0.283077     37800.0   \n",
       "          1000-1500       0.942362             0.065746     21100.0   \n",
       "          >1500           2.320675            15.611814     12500.0   \n",
       "\n",
       "                     edge_density  total_core_area  \\\n",
       "class_val elev-zone                                  \n",
       "1         <1000         26.978462             87.0   \n",
       "          1000-1500      7.056761              2.0   \n",
       "          >1500          1.054852              0.0   \n",
       "2         <1000         51.950769           2722.0   \n",
       "          1000-1500     39.206662            618.0   \n",
       "          >1500         33.544304             48.0   \n",
       "3         <1000         26.276923            212.0   \n",
       "          1000-1500     41.376288           1283.0   \n",
       "          >1500         19.620253              6.0   \n",
       "4         <1000          4.652308              4.0   \n",
       "          1000-1500      4.624151              0.0   \n",
       "          >1500         26.371308             44.0   \n",
       "\n",
       "                     core_area_proportion_of_landscape  \\\n",
       "class_val elev-zone                                      \n",
       "1         <1000                               1.070769   \n",
       "          1000-1500                           0.043831   \n",
       "          >1500                               0.000000   \n",
       "2         <1000                              33.501538   \n",
       "          1000-1500                          13.543721   \n",
       "          >1500                              10.126582   \n",
       "3         <1000                               2.609231   \n",
       "          1000-1500                          28.117467   \n",
       "          >1500                               1.265823   \n",
       "4         <1000                               0.049231   \n",
       "          1000-1500                           0.000000   \n",
       "          >1500                               9.282700   \n",
       "\n",
       "                     number_of_disjunct_core_areas  ...  \\\n",
       "class_val elev-zone                                 ...   \n",
       "1         <1000                                 17  ...   \n",
       "          1000-1500                              2  ...   \n",
       "          >1500                                  0  ...   \n",
       "2         <1000                                 81  ...   \n",
       "          1000-1500                             56  ...   \n",
       "          >1500                                  6  ...   \n",
       "3         <1000                                 34  ...   \n",
       "          1000-1500                             37  ...   \n",
       "          >1500                                  2  ...   \n",
       "4         <1000                                  2  ...   \n",
       "          1000-1500                              0  ...   \n",
       "          >1500                                  6  ...   \n",
       "\n",
       "                     euclidean_nearest_neighbor_md  \\\n",
       "class_val elev-zone                                  \n",
       "1         <1000                         223.606798   \n",
       "          1000-1500                     360.555128   \n",
       "          >1500                        2202.271555   \n",
       "2         <1000                         200.000000   \n",
       "          1000-1500                     200.000000   \n",
       "          >1500                         200.000000   \n",
       "3         <1000                         223.606798   \n",
       "          1000-1500                     223.606798   \n",
       "          >1500                         282.842712   \n",
       "4         <1000                         447.213595   \n",
       "          1000-1500                     447.213595   \n",
       "          >1500                         223.606798   \n",
       "\n",
       "                     euclidean_nearest_neighbor_ra  \\\n",
       "class_val elev-zone                                  \n",
       "1         <1000                         561.577311   \n",
       "          1000-1500                    2216.609195   \n",
       "          >1500                           0.000000   \n",
       "2         <1000                         694.427191   \n",
       "          1000-1500                     561.577311   \n",
       "          >1500                         300.000000   \n",
       "3         <1000                         528.010989   \n",
       "          1000-1500                     160.555128   \n",
       "          >1500                        1052.996409   \n",
       "4         <1000                        1624.828759   \n",
       "          1000-1500                    1569.180601   \n",
       "          >1500                         247.213595   \n",
       "\n",
       "                     euclidean_nearest_neighbor_sd  \\\n",
       "class_val elev-zone                                  \n",
       "1         <1000                         109.128542   \n",
       "          1000-1500                     435.754483   \n",
       "          >1500                           0.000000   \n",
       "2         <1000                         135.571676   \n",
       "          1000-1500                      87.023996   \n",
       "          >1500                         105.269873   \n",
       "3         <1000                          95.872336   \n",
       "          1000-1500                      44.752789   \n",
       "          >1500                         331.304373   \n",
       "4         <1000                         318.697963   \n",
       "          1000-1500                     354.559752   \n",
       "          >1500                          74.100090   \n",
       "\n",
       "                     euclidean_nearest_neighbor_cv  disjunct_core_area_mn  \\\n",
       "class_val elev-zone                                                         \n",
       "1         <1000                          39.692040               5.117647   \n",
       "          1000-1500                      88.662791               1.000000   \n",
       "          >1500                           0.000000                    NaN   \n",
       "2         <1000                          55.309188              33.604938   \n",
       "          1000-1500                      37.075659              11.035714   \n",
       "          >1500                          36.638140               8.000000   \n",
       "3         <1000                          34.919733               6.235294   \n",
       "          1000-1500                      18.862749              34.675676   \n",
       "          >1500                          76.908158               3.000000   \n",
       "4         <1000                          58.713471               2.000000   \n",
       "          1000-1500                      66.120768                    NaN   \n",
       "          >1500                          29.382577               7.333333   \n",
       "\n",
       "                     disjunct_core_area_am  disjunct_core_area_md  \\\n",
       "class_val elev-zone                                                 \n",
       "1         <1000                  21.068966                    1.0   \n",
       "          1000-1500               1.000000                    1.0   \n",
       "          >1500                        NaN                    NaN   \n",
       "2         <1000                 935.440852                    2.0   \n",
       "          1000-1500              46.148867                    3.5   \n",
       "          >1500                  12.500000                    8.0   \n",
       "3         <1000                  24.688679                    3.0   \n",
       "          1000-1500             226.632112                    4.0   \n",
       "          >1500                   4.333333                    3.0   \n",
       "4         <1000                   2.000000                    2.0   \n",
       "          1000-1500                    NaN                    NaN   \n",
       "          >1500                  13.181818                    4.5   \n",
       "\n",
       "                     disjunct_core_area_ra  disjunct_core_area_sd  \\\n",
       "class_val elev-zone                                                 \n",
       "1         <1000                       31.0               9.035110   \n",
       "          1000-1500                    0.0               0.000000   \n",
       "          >1500                        NaN                    NaN   \n",
       "2         <1000                     1525.0             174.086588   \n",
       "          1000-1500                  128.0              19.684987   \n",
       "          >1500                       15.0               6.000000   \n",
       "3         <1000                       62.0              10.726709   \n",
       "          1000-1500                  387.0              81.585655   \n",
       "          >1500                        4.0               2.000000   \n",
       "4         <1000                        0.0               0.000000   \n",
       "          1000-1500                    NaN                    NaN   \n",
       "          >1500                       20.0               6.548961   \n",
       "\n",
       "                     disjunct_core_area_cv  \n",
       "class_val elev-zone                         \n",
       "1         <1000                 176.548132  \n",
       "          1000-1500               0.000000  \n",
       "          >1500                        NaN  \n",
       "2         <1000                 518.038709  \n",
       "          1000-1500             178.375289  \n",
       "          >1500                  75.000000  \n",
       "3         <1000                 172.032124  \n",
       "          1000-1500             235.282092  \n",
       "          >1500                  66.666667  \n",
       "4         <1000                   0.000000  \n",
       "          1000-1500                    NaN  \n",
       "          >1500                  89.304012  \n",
       "\n",
       "[12 rows x 66 columns]"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "za.compute_class_metrics_df()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6b8094e2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[########################################] | 100% Completed | 2.17 ss\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>total_area</th>\n",
       "      <th>number_of_patches</th>\n",
       "      <th>patch_density</th>\n",
       "      <th>largest_patch_index</th>\n",
       "      <th>total_edge</th>\n",
       "      <th>edge_density</th>\n",
       "      <th>total_core_area</th>\n",
       "      <th>number_of_disjunct_core_areas</th>\n",
       "      <th>landscape_shape_index</th>\n",
       "      <th>effective_mesh_size</th>\n",
       "      <th>...</th>\n",
       "      <th>euclidean_nearest_neighbor_md</th>\n",
       "      <th>euclidean_nearest_neighbor_ra</th>\n",
       "      <th>euclidean_nearest_neighbor_sd</th>\n",
       "      <th>euclidean_nearest_neighbor_cv</th>\n",
       "      <th>disjunct_core_area_mn</th>\n",
       "      <th>disjunct_core_area_am</th>\n",
       "      <th>disjunct_core_area_md</th>\n",
       "      <th>disjunct_core_area_ra</th>\n",
       "      <th>disjunct_core_area_sd</th>\n",
       "      <th>disjunct_core_area_cv</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>elev-zone</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>&lt;1000</th>\n",
       "      <td>8125.0</td>\n",
       "      <td>488</td>\n",
       "      <td>6.006154</td>\n",
       "      <td>67.876923</td>\n",
       "      <td>446300.0</td>\n",
       "      <td>54.929231</td>\n",
       "      <td>3025.0</td>\n",
       "      <td>134</td>\n",
       "      <td>14.715470</td>\n",
       "      <td>3761.643938</td>\n",
       "      <td>...</td>\n",
       "      <td>223.606798</td>\n",
       "      <td>1624.828759</td>\n",
       "      <td>189.354754</td>\n",
       "      <td>59.810237</td>\n",
       "      <td>22.574627</td>\n",
       "      <td>770.677127</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1525.0</td>\n",
       "      <td>136.180761</td>\n",
       "      <td>603.247007</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1000-1500</th>\n",
       "      <td>4563.0</td>\n",
       "      <td>178</td>\n",
       "      <td>3.900942</td>\n",
       "      <td>46.833224</td>\n",
       "      <td>210500.0</td>\n",
       "      <td>46.131931</td>\n",
       "      <td>1903.0</td>\n",
       "      <td>95</td>\n",
       "      <td>9.988971</td>\n",
       "      <td>1144.435021</td>\n",
       "      <td>...</td>\n",
       "      <td>223.606798</td>\n",
       "      <td>2216.609195</td>\n",
       "      <td>323.048708</td>\n",
       "      <td>85.277595</td>\n",
       "      <td>20.031579</td>\n",
       "      <td>150.305451</td>\n",
       "      <td>4.0</td>\n",
       "      <td>387.0</td>\n",
       "      <td>54.403067</td>\n",
       "      <td>271.586516</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>&gt;1500</th>\n",
       "      <td>474.0</td>\n",
       "      <td>39</td>\n",
       "      <td>8.227848</td>\n",
       "      <td>17.932489</td>\n",
       "      <td>19100.0</td>\n",
       "      <td>40.295359</td>\n",
       "      <td>98.0</td>\n",
       "      <td>14</td>\n",
       "      <td>5.897727</td>\n",
       "      <td>52.371308</td>\n",
       "      <td>...</td>\n",
       "      <td>223.606798</td>\n",
       "      <td>2002.271555</td>\n",
       "      <td>471.829939</td>\n",
       "      <td>109.526193</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>9.600000</td>\n",
       "      <td>5.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>6.094494</td>\n",
       "      <td>87.064200</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3 rows × 71 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           total_area  number_of_patches  patch_density  largest_patch_index  \\\n",
       "elev-zone                                                                      \n",
       "<1000          8125.0                488       6.006154            67.876923   \n",
       "1000-1500      4563.0                178       3.900942            46.833224   \n",
       ">1500           474.0                 39       8.227848            17.932489   \n",
       "\n",
       "           total_edge  edge_density  total_core_area  \\\n",
       "elev-zone                                              \n",
       "<1000        446300.0     54.929231           3025.0   \n",
       "1000-1500    210500.0     46.131931           1903.0   \n",
       ">1500         19100.0     40.295359             98.0   \n",
       "\n",
       "           number_of_disjunct_core_areas  landscape_shape_index  \\\n",
       "elev-zone                                                         \n",
       "<1000                                134              14.715470   \n",
       "1000-1500                             95               9.988971   \n",
       ">1500                                 14               5.897727   \n",
       "\n",
       "           effective_mesh_size  ...  euclidean_nearest_neighbor_md  \\\n",
       "elev-zone                       ...                                  \n",
       "<1000              3761.643938  ...                     223.606798   \n",
       "1000-1500          1144.435021  ...                     223.606798   \n",
       ">1500                52.371308  ...                     223.606798   \n",
       "\n",
       "           euclidean_nearest_neighbor_ra  euclidean_nearest_neighbor_sd  \\\n",
       "elev-zone                                                                 \n",
       "<1000                        1624.828759                     189.354754   \n",
       "1000-1500                    2216.609195                     323.048708   \n",
       ">1500                        2002.271555                     471.829939   \n",
       "\n",
       "           euclidean_nearest_neighbor_cv  disjunct_core_area_mn  \\\n",
       "elev-zone                                                         \n",
       "<1000                          59.810237              22.574627   \n",
       "1000-1500                      85.277595              20.031579   \n",
       ">1500                         109.526193               7.000000   \n",
       "\n",
       "           disjunct_core_area_am  disjunct_core_area_md  \\\n",
       "elev-zone                                                 \n",
       "<1000                 770.677127                    2.0   \n",
       "1000-1500             150.305451                    4.0   \n",
       ">1500                   9.600000                    5.0   \n",
       "\n",
       "           disjunct_core_area_ra  disjunct_core_area_sd  disjunct_core_area_cv  \n",
       "elev-zone                                                                       \n",
       "<1000                     1525.0             136.180761             603.247007  \n",
       "1000-1500                  387.0              54.403067             271.586516  \n",
       ">1500                       20.0               6.094494              87.064200  \n",
       "\n",
       "[3 rows x 71 columns]"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "za.compute_landscape_metrics_df()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "582bfa0c",
   "metadata": {},
   "source": [
    "We can also use the `plot_metric` method to plot the metrics computed for each zone, e.g., how the proportion of landscape occupied by the land use class value `1` (urban) changes accross elevation zones"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a227db3e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[########################################] | 100% Completed | 106.19 ms\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Axes: ylabel='PLAND'>"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "za.plot_metric(\"proportion_of_landscape\", class_val=URBAN_CLASS_VAL)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "14ea3358",
   "metadata": {},
   "source": [
    "Like in the spatio-temporal analysis, the plots at the landscape level can obtained by not providing any `class_val` argument.\n",
    "\n",
    "In order to visualize such information in space, the zonal statistics can be computed in the form of a geo-data frame with the `compute_zonal_statistics_gdf` method as in:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fc3dd1f2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[########################################] | 100% Completed | 101.27 ms\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>edge_density</th>\n",
       "      <th>proportion_of_landscape</th>\n",
       "      <th>geometry</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>elev-zone</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1000-1500</th>\n",
       "      <td>7.056761</td>\n",
       "      <td>2.564103</td>\n",
       "      <td>POLYGON ((2563900.000 1160700.000, 2563900.000...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>&lt;1000</th>\n",
       "      <td>26.978462</td>\n",
       "      <td>11.212308</td>\n",
       "      <td>MULTIPOLYGON (((2560900.000 1150500.000, 25607...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>&gt;1500</th>\n",
       "      <td>1.054852</td>\n",
       "      <td>0.421941</td>\n",
       "      <td>MULTIPOLYGON (((2566300.000 1151500.000, 25665...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           edge_density  proportion_of_landscape  \\\n",
       "elev-zone                                          \n",
       "1000-1500      7.056761                 2.564103   \n",
       "<1000         26.978462                11.212308   \n",
       ">1500          1.054852                 0.421941   \n",
       "\n",
       "                                                    geometry  \n",
       "elev-zone                                                     \n",
       "1000-1500  POLYGON ((2563900.000 1160700.000, 2563900.000...  \n",
       "<1000      MULTIPOLYGON (((2560900.000 1150500.000, 25607...  \n",
       ">1500      MULTIPOLYGON (((2566300.000 1151500.000, 25665...  "
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metrics = [\"proportion_of_landscape\", \"edge_density\"]\n",
    "zonal_statistics_gdf = za.compute_zonal_statistics_gdf(\n",
    "    metrics=metrics, class_val=URBAN_CLASS_VAL\n",
    ")\n",
    "\n",
    "zonal_statistics_gdf.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8376363d",
   "metadata": {},
   "source": [
    "the computed metrics are essentially the same as those obtained using the `compute_class_metrics_df` or `compute_landscape_metrics_df` (depending on whether a `class_val` argument is provided or not), with an additional column featuring the vector geometry of each zone. This actually corresponds to a geopandas geo-data frame, and as such, we can use [its `geopandas.GeoDataFrame.explore` method](https://geopandas.org/en/stable/docs/reference/api/geopandas.GeoDataFrame.explore.html) to obtain an interactive map as in:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "de56e83e",
   "metadata": {},
   "outputs": [
    {
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       "            &lt;style&gt;\n",
       "                #map_2f07d77da6f9cf78db3576a48123b0ee {\n",
       "                    position: relative;\n",
       "                    width: 100.0%;\n",
       "                    height: 100.0%;\n",
       "                    left: 0.0%;\n",
       "                    top: 0.0%;\n",
       "                }\n",
       "                .leaflet-container { font-size: 1rem; }\n",
       "            &lt;/style&gt;\n",
       "        \n",
       "    \n",
       "                    &lt;style&gt;\n",
       "                        .foliumtooltip {\n",
       "                            \n",
       "                        }\n",
       "                       .foliumtooltip table{\n",
       "                            margin: auto;\n",
       "                        }\n",
       "                        .foliumtooltip tr{\n",
       "                            text-align: left;\n",
       "                        }\n",
       "                        .foliumtooltip th{\n",
       "                            padding: 2px; padding-right: 8px;\n",
       "                        }\n",
       "                    &lt;/style&gt;\n",
       "            \n",
       "&lt;/head&gt;\n",
       "&lt;body&gt;\n",
       "    \n",
       "    \n",
       "            &lt;div class=&quot;folium-map&quot; id=&quot;map_2f07d77da6f9cf78db3576a48123b0ee&quot; &gt;&lt;/div&gt;\n",
       "        \n",
       "&lt;/body&gt;\n",
       "&lt;script&gt;\n",
       "    \n",
       "    \n",
       "            var map_2f07d77da6f9cf78db3576a48123b0ee = L.map(\n",
       "                &quot;map_2f07d77da6f9cf78db3576a48123b0ee&quot;,\n",
       "                {\n",
       "                    center: [46.551446055528224, 6.910719434416071],\n",
       "                    crs: L.CRS.EPSG3857,\n",
       "                    zoom: 10,\n",
       "                    zoomControl: true,\n",
       "                    preferCanvas: false,\n",
       "                }\n",
       "            );\n",
       "            L.control.scale().addTo(map_2f07d77da6f9cf78db3576a48123b0ee);\n",
       "\n",
       "            \n",
       "\n",
       "        \n",
       "    \n",
       "            var tile_layer_51718d1dc57878d930ad66a055daa2b1 = L.tileLayer(\n",
       "                &quot;https://{s}.tile.openstreetmap.org/{z}/{x}/{y}.png&quot;,\n",
       "                {&quot;attribution&quot;: &quot;Data by \\u0026copy; \\u003ca target=\\&quot;_blank\\&quot; href=\\&quot;http://openstreetmap.org\\&quot;\\u003eOpenStreetMap\\u003c/a\\u003e, under \\u003ca target=\\&quot;_blank\\&quot; href=\\&quot;http://www.openstreetmap.org/copyright\\&quot;\\u003eODbL\\u003c/a\\u003e.&quot;, &quot;detectRetina&quot;: false, &quot;maxNativeZoom&quot;: 18, &quot;maxZoom&quot;: 18, &quot;minZoom&quot;: 0, &quot;noWrap&quot;: false, &quot;opacity&quot;: 1, &quot;subdomains&quot;: &quot;abc&quot;, &quot;tms&quot;: false}\n",
       "            ).addTo(map_2f07d77da6f9cf78db3576a48123b0ee);\n",
       "        \n",
       "    \n",
       "            map_2f07d77da6f9cf78db3576a48123b0ee.fitBounds(\n",
       "                [[46.476088309446936, 6.811732168599031], [46.62680380160951, 7.009706700233111]],\n",
       "                {}\n",
       "            );\n",
       "        \n",
       "    \n",
       "        function geo_json_f2957e28b94bf9fc52978944882bb188_styler(feature) {\n",
       "            switch(feature.id) {\n",
       "                default:\n",
       "                    return {&quot;fillOpacity&quot;: 0.5, &quot;weight&quot;: 2};\n",
       "            }\n",
       "        }\n",
       "        function geo_json_f2957e28b94bf9fc52978944882bb188_highlighter(feature) {\n",
       "            switch(feature.id) {\n",
       "                default:\n",
       "                    return {&quot;fillOpacity&quot;: 0.75};\n",
       "            }\n",
       "        }\n",
       "        function geo_json_f2957e28b94bf9fc52978944882bb188_pointToLayer(feature, latlng) {\n",
       "            var opts = {&quot;bubblingMouseEvents&quot;: true, &quot;color&quot;: &quot;#3388ff&quot;, &quot;dashArray&quot;: null, &quot;dashOffset&quot;: null, &quot;fill&quot;: true, &quot;fillColor&quot;: &quot;#3388ff&quot;, &quot;fillOpacity&quot;: 0.2, &quot;fillRule&quot;: &quot;evenodd&quot;, &quot;lineCap&quot;: &quot;round&quot;, &quot;lineJoin&quot;: &quot;round&quot;, &quot;opacity&quot;: 1.0, &quot;radius&quot;: 2, &quot;stroke&quot;: true, &quot;weight&quot;: 3};\n",
       "            \n",
       "            let style = geo_json_f2957e28b94bf9fc52978944882bb188_styler(feature)\n",
       "            Object.assign(opts, style)\n",
       "            \n",
       "            return new L.CircleMarker(latlng, opts)\n",
       "        }\n",
       "\n",
       "        function geo_json_f2957e28b94bf9fc52978944882bb188_onEachFeature(feature, layer) {\n",
       "            layer.on({\n",
       "                mouseout: function(e) {\n",
       "                    if(typeof e.target.setStyle === &quot;function&quot;){\n",
       "                        geo_json_f2957e28b94bf9fc52978944882bb188.resetStyle(e.target);\n",
       "                    }\n",
       "                },\n",
       "                mouseover: function(e) {\n",
       "                    if(typeof e.target.setStyle === &quot;function&quot;){\n",
       "                        const highlightStyle = geo_json_f2957e28b94bf9fc52978944882bb188_highlighter(e.target.feature)\n",
       "                        e.target.setStyle(highlightStyle);\n",
       "                    }\n",
       "                },\n",
       "            });\n",
       "        };\n",
       "        var geo_json_f2957e28b94bf9fc52978944882bb188 = L.geoJson(null, {\n",
       "                onEachFeature: geo_json_f2957e28b94bf9fc52978944882bb188_onEachFeature,\n",
       "            \n",
       "                style: geo_json_f2957e28b94bf9fc52978944882bb188_styler,\n",
       "                pointToLayer: geo_json_f2957e28b94bf9fc52978944882bb188_pointToLayer\n",
       "        });\n",
       "\n",
       "        function geo_json_f2957e28b94bf9fc52978944882bb188_add (data) {\n",
       "            geo_json_f2957e28b94bf9fc52978944882bb188\n",
       "                .addData(data)\n",
       "                .addTo(map_2f07d77da6f9cf78db3576a48123b0ee);\n",
       "        }\n",
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&quot;properties&quot;: {&quot;edge_density&quot;: 1.0548523206751055, &quot;elev-zone&quot;: &quot;\\u003e1500&quot;, &quot;proportion_of_landscape&quot;: 0.4219409282700422}, &quot;type&quot;: &quot;Feature&quot;}], &quot;type&quot;: &quot;FeatureCollection&quot;});\n",
       "\n",
       "        \n",
       "    \n",
       "    geo_json_f2957e28b94bf9fc52978944882bb188.bindTooltip(\n",
       "    function(layer){\n",
       "    let div = L.DomUtil.create(&#x27;div&#x27;);\n",
       "    \n",
       "    let handleObject = feature=&gt;typeof(feature)==&#x27;object&#x27; ? JSON.stringify(feature) : feature;\n",
       "    let fields = [&quot;elev-zone&quot;, &quot;edge_density&quot;, &quot;proportion_of_landscape&quot;];\n",
       "    let aliases = [&quot;elev-zone&quot;, &quot;edge_density&quot;, &quot;proportion_of_landscape&quot;];\n",
       "    let table = &#x27;&lt;table&gt;&#x27; +\n",
       "        String(\n",
       "        fields.map(\n",
       "        (v,i)=&gt;\n",
       "        `&lt;tr&gt;\n",
       "            &lt;th&gt;${aliases[i]}&lt;/th&gt;\n",
       "            \n",
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      ],
      "text/plain": [
       "<folium.folium.Map at 0x770f07efd050>"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "zonal_statistics_gdf.explore()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f14f7aec",
   "metadata": {},
   "source": [
    "## Spatio-temporal zonal analysis\n",
    "\n",
    "We might also be interested in performing the same zonal analysis at different points in time. This is why pylandstats features an additional `SpatioTemporalZonalAnalysis` analysis class - as well as `SpatioTemporalBufferAnalysis` and `SpatioTemporalZonalGridAnalysis`. See the notebook [04-spatiotemporal-zonal-analysis.ipynb](https://github.com/martibosch/pylandstats-notebooks/blob/main/notebooks/04-spatiotemporal-zonal-analysis.ipynb) for a more thorough demonstration.\n",
    "\n",
    "Let us take the sequence of landscapes `input_filepaths` from [the spatio-temporal analysis above](#spatiotemporal-analysis) and let us use again the `dates` argument to specify the dates that correspond to each landscape.\n",
    "Let us also take the latitude and longitude of the center of Lausanne as well as the elevation zones from [the zonal analysis above](#zonal-analysis). Now we can construct our `SpatioTemporalZonalAnalysis` instance and evaluate the sensitive of our spatio-temporal analysis to the extent of the map:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e96c8eff",
   "metadata": {},
   "outputs": [],
   "source": [
    "stza = pls.SpatioTemporalZonalAnalysis(\n",
    "    input_filepaths, elev_zones_filepath, dates=years, zone_index=\"elev-zone\"\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "63037e1a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[######                                  ] | 16% Completed | 1.36 sms"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/martibosch/mambaforge/envs/pylandstats/lib/python3.11/site-packages/numpy/core/fromnumeric.py:3504: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/martibosch/mambaforge/envs/pylandstats/lib/python3.11/site-packages/numpy/core/_methods.py:129: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  ret = ret.dtype.type(ret / rcount)\n",
      "/home/martibosch/mambaforge/envs/pylandstats/lib/python3.11/site-packages/numpy/core/fromnumeric.py:3504: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/martibosch/mambaforge/envs/pylandstats/lib/python3.11/site-packages/numpy/core/_methods.py:129: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  ret = ret.dtype.type(ret / rcount)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[####################                    ] | 50% Completed | 2.03 s"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/martibosch/mambaforge/envs/pylandstats/lib/python3.11/site-packages/numpy/core/fromnumeric.py:3504: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/martibosch/mambaforge/envs/pylandstats/lib/python3.11/site-packages/numpy/core/_methods.py:129: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  ret = ret.dtype.type(ret / rcount)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[##########################              ] | 66% Completed | 2.46 s"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/martibosch/data-science/landscape-connectivity-barcelona/src/pylandstats/pylandstats/landscape.py:1338: RuntimeWarning: Class 1 has less than 2 patches. Euclidean-nearest-neighbor might contain nan values\n",
      "  warnings.warn(\n",
      "/home/martibosch/mambaforge/envs/pylandstats/lib/python3.11/site-packages/numpy/core/fromnumeric.py:3504: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/martibosch/mambaforge/envs/pylandstats/lib/python3.11/site-packages/numpy/core/_methods.py:129: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  ret = ret.dtype.type(ret / rcount)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[########################################] | 100% Completed | 2.66 s\n"
     ]
    },
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       "      <th>total_area</th>\n",
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       "      <td>680.0</td>\n",
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       "      <td>3.076923</td>\n",
       "      <td>0.984615</td>\n",
       "      <td>191300.0</td>\n",
       "      <td>23.544615</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0.332308</td>\n",
       "      <td>9</td>\n",
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       "      <td>41.006394</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>8.259259</td>\n",
       "      <td>1.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>3.972125</td>\n",
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       "      <td>786.0</td>\n",
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       "      <td>3.064615</td>\n",
       "      <td>1.144615</td>\n",
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       "      <td>25.316923</td>\n",
       "      <td>48.0</td>\n",
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       "      <td>11.125000</td>\n",
       "      <td>2.5</td>\n",
       "      <td>16.0</td>\n",
       "      <td>5.509991</td>\n",
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       "      <td>911.0</td>\n",
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       "      <td>31.0</td>\n",
       "      <td>9.035110</td>\n",
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       "      <td>1.073855</td>\n",
       "      <td>0.372562</td>\n",
       "      <td>31200.0</td>\n",
       "      <td>6.837607</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.021915</td>\n",
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       "      <td>355.941215</td>\n",
       "      <td>85.601955</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.0</td>\n",
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       "      <td>0.460224</td>\n",
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       "      <td>2.0</td>\n",
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       "      <td>435.754483</td>\n",
       "      <td>88.662791</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.210970</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.210970</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>0.210970</td>\n",
       "      <td>500.0</td>\n",
       "      <td>1.054852</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
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       "      <td>0.000000</td>\n",
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       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>500.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>500.0</td>\n",
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       "      <td>0.0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>2202.271555</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>71.741538</td>\n",
       "      <td>389800.0</td>\n",
       "      <td>47.975385</td>\n",
       "      <td>3121.0</td>\n",
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       "      <td>64</td>\n",
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       "      <td>200.000000</td>\n",
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       "      <td>164.098358</td>\n",
       "      <td>63.032003</td>\n",
       "      <td>48.765625</td>\n",
       "      <td>905.413970</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1322.0</td>\n",
       "      <td>204.389315</td>\n",
       "      <td>419.125797</td>\n",
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       "      <td>21</td>\n",
       "      <td>0.258462</td>\n",
       "      <td>70.535385</td>\n",
       "      <td>399800.0</td>\n",
       "      <td>49.206154</td>\n",
       "      <td>2996.0</td>\n",
       "      <td>36.873846</td>\n",
       "      <td>72</td>\n",
       "      <td>...</td>\n",
       "      <td>200.000000</td>\n",
       "      <td>694.427191</td>\n",
       "      <td>148.848153</td>\n",
       "      <td>58.176098</td>\n",
       "      <td>41.611111</td>\n",
       "      <td>927.955941</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1417.0</td>\n",
       "      <td>192.046331</td>\n",
       "      <td>461.526563</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2004</th>\n",
       "      <td>5877.0</td>\n",
       "      <td>72.332308</td>\n",
       "      <td>22</td>\n",
       "      <td>0.270769</td>\n",
       "      <td>69.304615</td>\n",
       "      <td>412100.0</td>\n",
       "      <td>50.720000</td>\n",
       "      <td>2847.0</td>\n",
       "      <td>35.040000</td>\n",
       "      <td>75</td>\n",
       "      <td>...</td>\n",
       "      <td>200.000000</td>\n",
       "      <td>694.427191</td>\n",
       "      <td>145.890605</td>\n",
       "      <td>57.591673</td>\n",
       "      <td>37.960000</td>\n",
       "      <td>1462.589041</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2017.0</td>\n",
       "      <td>232.548744</td>\n",
       "      <td>612.615237</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013</th>\n",
       "      <td>5753.0</td>\n",
       "      <td>70.806154</td>\n",
       "      <td>26</td>\n",
       "      <td>0.320000</td>\n",
       "      <td>67.876923</td>\n",
       "      <td>422100.0</td>\n",
       "      <td>51.950769</td>\n",
       "      <td>2722.0</td>\n",
       "      <td>33.501538</td>\n",
       "      <td>81</td>\n",
       "      <td>...</td>\n",
       "      <td>200.000000</td>\n",
       "      <td>694.427191</td>\n",
       "      <td>135.571676</td>\n",
       "      <td>55.309188</td>\n",
       "      <td>33.604938</td>\n",
       "      <td>935.440852</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1525.0</td>\n",
       "      <td>174.086588</td>\n",
       "      <td>518.038709</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">1000-1500</th>\n",
       "      <th>1980</th>\n",
       "      <td>1915.0</td>\n",
       "      <td>41.968004</td>\n",
       "      <td>34</td>\n",
       "      <td>0.745124</td>\n",
       "      <td>16.392724</td>\n",
       "      <td>186400.0</td>\n",
       "      <td>40.850318</td>\n",
       "      <td>673.0</td>\n",
       "      <td>14.749069</td>\n",
       "      <td>51</td>\n",
       "      <td>...</td>\n",
       "      <td>200.000000</td>\n",
       "      <td>470.820393</td>\n",
       "      <td>84.358395</td>\n",
       "      <td>34.974357</td>\n",
       "      <td>13.196078</td>\n",
       "      <td>69.430906</td>\n",
       "      <td>3.0</td>\n",
       "      <td>184.0</td>\n",
       "      <td>27.241131</td>\n",
       "      <td>206.433531</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1992</th>\n",
       "      <td>1840.0</td>\n",
       "      <td>40.324348</td>\n",
       "      <td>40</td>\n",
       "      <td>0.876616</td>\n",
       "      <td>12.579443</td>\n",
       "      <td>183000.0</td>\n",
       "      <td>40.105194</td>\n",
       "      <td>637.0</td>\n",
       "      <td>13.960114</td>\n",
       "      <td>51</td>\n",
       "      <td>...</td>\n",
       "      <td>200.000000</td>\n",
       "      <td>561.577311</td>\n",
       "      <td>92.925495</td>\n",
       "      <td>38.382304</td>\n",
       "      <td>12.490196</td>\n",
       "      <td>48.864992</td>\n",
       "      <td>4.0</td>\n",
       "      <td>132.0</td>\n",
       "      <td>21.314979</td>\n",
       "      <td>170.653679</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2004</th>\n",
       "      <td>1818.0</td>\n",
       "      <td>39.842209</td>\n",
       "      <td>42</td>\n",
       "      <td>0.920447</td>\n",
       "      <td>12.469866</td>\n",
       "      <td>179300.0</td>\n",
       "      <td>39.294324</td>\n",
       "      <td>630.0</td>\n",
       "      <td>13.806706</td>\n",
       "      <td>56</td>\n",
       "      <td>...</td>\n",
       "      <td>200.000000</td>\n",
       "      <td>561.577311</td>\n",
       "      <td>89.403555</td>\n",
       "      <td>37.602106</td>\n",
       "      <td>11.250000</td>\n",
       "      <td>46.771429</td>\n",
       "      <td>3.5</td>\n",
       "      <td>130.0</td>\n",
       "      <td>19.990399</td>\n",
       "      <td>177.692440</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013</th>\n",
       "      <td>1800.0</td>\n",
       "      <td>39.447732</td>\n",
       "      <td>45</td>\n",
       "      <td>0.986193</td>\n",
       "      <td>12.447951</td>\n",
       "      <td>178900.0</td>\n",
       "      <td>39.206662</td>\n",
       "      <td>618.0</td>\n",
       "      <td>13.543721</td>\n",
       "      <td>56</td>\n",
       "      <td>...</td>\n",
       "      <td>200.000000</td>\n",
       "      <td>561.577311</td>\n",
       "      <td>87.023996</td>\n",
       "      <td>37.075659</td>\n",
       "      <td>11.035714</td>\n",
       "      <td>46.148867</td>\n",
       "      <td>3.5</td>\n",
       "      <td>128.0</td>\n",
       "      <td>19.684987</td>\n",
       "      <td>178.375289</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">&gt;1500</th>\n",
       "      <th>1980</th>\n",
       "      <td>246.0</td>\n",
       "      <td>51.898734</td>\n",
       "      <td>11</td>\n",
       "      <td>2.320675</td>\n",
       "      <td>18.565401</td>\n",
       "      <td>15800.0</td>\n",
       "      <td>33.333333</td>\n",
       "      <td>52.0</td>\n",
       "      <td>10.970464</td>\n",
       "      <td>6</td>\n",
       "      <td>...</td>\n",
       "      <td>200.000000</td>\n",
       "      <td>300.000000</td>\n",
       "      <td>99.295132</td>\n",
       "      <td>35.687854</td>\n",
       "      <td>8.666667</td>\n",
       "      <td>13.615385</td>\n",
       "      <td>9.5</td>\n",
       "      <td>16.0</td>\n",
       "      <td>6.548961</td>\n",
       "      <td>75.564933</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1992</th>\n",
       "      <td>240.0</td>\n",
       "      <td>50.632911</td>\n",
       "      <td>11</td>\n",
       "      <td>2.320675</td>\n",
       "      <td>18.354430</td>\n",
       "      <td>15800.0</td>\n",
       "      <td>33.333333</td>\n",
       "      <td>51.0</td>\n",
       "      <td>10.759494</td>\n",
       "      <td>6</td>\n",
       "      <td>...</td>\n",
       "      <td>200.000000</td>\n",
       "      <td>300.000000</td>\n",
       "      <td>99.295132</td>\n",
       "      <td>35.687854</td>\n",
       "      <td>8.500000</td>\n",
       "      <td>13.235294</td>\n",
       "      <td>9.5</td>\n",
       "      <td>15.0</td>\n",
       "      <td>6.344289</td>\n",
       "      <td>74.638691</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2004</th>\n",
       "      <td>239.0</td>\n",
       "      <td>50.421941</td>\n",
       "      <td>11</td>\n",
       "      <td>2.320675</td>\n",
       "      <td>18.143460</td>\n",
       "      <td>15600.0</td>\n",
       "      <td>32.911392</td>\n",
       "      <td>51.0</td>\n",
       "      <td>10.759494</td>\n",
       "      <td>6</td>\n",
       "      <td>...</td>\n",
       "      <td>200.000000</td>\n",
       "      <td>300.000000</td>\n",
       "      <td>99.295132</td>\n",
       "      <td>35.687854</td>\n",
       "      <td>8.500000</td>\n",
       "      <td>13.235294</td>\n",
       "      <td>9.5</td>\n",
       "      <td>15.0</td>\n",
       "      <td>6.344289</td>\n",
       "      <td>74.638691</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013</th>\n",
       "      <td>237.0</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>11</td>\n",
       "      <td>2.320675</td>\n",
       "      <td>17.932489</td>\n",
       "      <td>15900.0</td>\n",
       "      <td>33.544304</td>\n",
       "      <td>48.0</td>\n",
       "      <td>10.126582</td>\n",
       "      <td>6</td>\n",
       "      <td>...</td>\n",
       "      <td>200.000000</td>\n",
       "      <td>300.000000</td>\n",
       "      <td>105.269873</td>\n",
       "      <td>36.638140</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>12.500000</td>\n",
       "      <td>8.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>75.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"12\" valign=\"top\">3</th>\n",
       "      <th rowspan=\"4\" valign=\"top\">&lt;1000</th>\n",
       "      <th>1980</th>\n",
       "      <td>1313.0</td>\n",
       "      <td>16.160000</td>\n",
       "      <td>152</td>\n",
       "      <td>1.870769</td>\n",
       "      <td>2.535385</td>\n",
       "      <td>214400.0</td>\n",
       "      <td>26.387692</td>\n",
       "      <td>198.0</td>\n",
       "      <td>2.436923</td>\n",
       "      <td>35</td>\n",
       "      <td>...</td>\n",
       "      <td>223.606798</td>\n",
       "      <td>408.276253</td>\n",
       "      <td>93.084816</td>\n",
       "      <td>34.090164</td>\n",
       "      <td>5.657143</td>\n",
       "      <td>24.606061</td>\n",
       "      <td>3.0</td>\n",
       "      <td>62.0</td>\n",
       "      <td>10.353586</td>\n",
       "      <td>183.017927</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1992</th>\n",
       "      <td>1328.0</td>\n",
       "      <td>16.344615</td>\n",
       "      <td>151</td>\n",
       "      <td>1.858462</td>\n",
       "      <td>2.535385</td>\n",
       "      <td>214100.0</td>\n",
       "      <td>26.350769</td>\n",
       "      <td>203.0</td>\n",
       "      <td>2.498462</td>\n",
       "      <td>35</td>\n",
       "      <td>...</td>\n",
       "      <td>223.606798</td>\n",
       "      <td>408.276253</td>\n",
       "      <td>91.579955</td>\n",
       "      <td>33.516447</td>\n",
       "      <td>5.800000</td>\n",
       "      <td>24.201970</td>\n",
       "      <td>3.0</td>\n",
       "      <td>62.0</td>\n",
       "      <td>10.331090</td>\n",
       "      <td>178.122248</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2004</th>\n",
       "      <td>1337.0</td>\n",
       "      <td>16.455385</td>\n",
       "      <td>150</td>\n",
       "      <td>1.846154</td>\n",
       "      <td>2.535385</td>\n",
       "      <td>212800.0</td>\n",
       "      <td>26.190769</td>\n",
       "      <td>212.0</td>\n",
       "      <td>2.609231</td>\n",
       "      <td>34</td>\n",
       "      <td>...</td>\n",
       "      <td>223.606798</td>\n",
       "      <td>528.010989</td>\n",
       "      <td>95.684598</td>\n",
       "      <td>35.227288</td>\n",
       "      <td>6.235294</td>\n",
       "      <td>24.754717</td>\n",
       "      <td>3.0</td>\n",
       "      <td>62.0</td>\n",
       "      <td>10.745885</td>\n",
       "      <td>172.339668</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013</th>\n",
       "      <td>1339.0</td>\n",
       "      <td>16.480000</td>\n",
       "      <td>151</td>\n",
       "      <td>1.858462</td>\n",
       "      <td>2.535385</td>\n",
       "      <td>213500.0</td>\n",
       "      <td>26.276923</td>\n",
       "      <td>212.0</td>\n",
       "      <td>2.609231</td>\n",
       "      <td>34</td>\n",
       "      <td>...</td>\n",
       "      <td>223.606798</td>\n",
       "      <td>528.010989</td>\n",
       "      <td>95.872336</td>\n",
       "      <td>34.919733</td>\n",
       "      <td>6.235294</td>\n",
       "      <td>24.688679</td>\n",
       "      <td>3.0</td>\n",
       "      <td>62.0</td>\n",
       "      <td>10.726709</td>\n",
       "      <td>172.032124</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">1000-1500</th>\n",
       "      <th>1980</th>\n",
       "      <td>2469.0</td>\n",
       "      <td>54.109139</td>\n",
       "      <td>46</td>\n",
       "      <td>1.008109</td>\n",
       "      <td>41.200964</td>\n",
       "      <td>193000.0</td>\n",
       "      <td>42.296735</td>\n",
       "      <td>1183.0</td>\n",
       "      <td>25.925926</td>\n",
       "      <td>39</td>\n",
       "      <td>...</td>\n",
       "      <td>200.000000</td>\n",
       "      <td>160.555128</td>\n",
       "      <td>42.456789</td>\n",
       "      <td>18.716277</td>\n",
       "      <td>30.333333</td>\n",
       "      <td>202.391378</td>\n",
       "      <td>5.0</td>\n",
       "      <td>354.0</td>\n",
       "      <td>72.243297</td>\n",
       "      <td>238.164717</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1992</th>\n",
       "      <td>2552.0</td>\n",
       "      <td>55.928117</td>\n",
       "      <td>38</td>\n",
       "      <td>0.832785</td>\n",
       "      <td>46.373000</td>\n",
       "      <td>193500.0</td>\n",
       "      <td>42.406312</td>\n",
       "      <td>1244.0</td>\n",
       "      <td>27.262766</td>\n",
       "      <td>37</td>\n",
       "      <td>...</td>\n",
       "      <td>200.000000</td>\n",
       "      <td>160.555128</td>\n",
       "      <td>45.191377</td>\n",
       "      <td>19.686685</td>\n",
       "      <td>33.621622</td>\n",
       "      <td>220.766881</td>\n",
       "      <td>5.0</td>\n",
       "      <td>378.0</td>\n",
       "      <td>79.322929</td>\n",
       "      <td>235.928326</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2004</th>\n",
       "      <td>2575.0</td>\n",
       "      <td>56.432172</td>\n",
       "      <td>41</td>\n",
       "      <td>0.898532</td>\n",
       "      <td>46.789393</td>\n",
       "      <td>189000.0</td>\n",
       "      <td>41.420118</td>\n",
       "      <td>1280.0</td>\n",
       "      <td>28.051720</td>\n",
       "      <td>38</td>\n",
       "      <td>...</td>\n",
       "      <td>200.000000</td>\n",
       "      <td>160.555128</td>\n",
       "      <td>44.190577</td>\n",
       "      <td>18.948529</td>\n",
       "      <td>33.684211</td>\n",
       "      <td>226.190625</td>\n",
       "      <td>4.0</td>\n",
       "      <td>387.0</td>\n",
       "      <td>80.525937</td>\n",
       "      <td>239.061377</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013</th>\n",
       "      <td>2583.0</td>\n",
       "      <td>56.607495</td>\n",
       "      <td>41</td>\n",
       "      <td>0.898532</td>\n",
       "      <td>46.833224</td>\n",
       "      <td>188800.0</td>\n",
       "      <td>41.376288</td>\n",
       "      <td>1283.0</td>\n",
       "      <td>28.117467</td>\n",
       "      <td>37</td>\n",
       "      <td>...</td>\n",
       "      <td>223.606798</td>\n",
       "      <td>160.555128</td>\n",
       "      <td>44.752789</td>\n",
       "      <td>18.862749</td>\n",
       "      <td>34.675676</td>\n",
       "      <td>226.632112</td>\n",
       "      <td>4.0</td>\n",
       "      <td>387.0</td>\n",
       "      <td>81.585655</td>\n",
       "      <td>235.282092</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">&gt;1500</th>\n",
       "      <th>1980</th>\n",
       "      <td>66.0</td>\n",
       "      <td>13.924051</td>\n",
       "      <td>12</td>\n",
       "      <td>2.531646</td>\n",
       "      <td>7.805907</td>\n",
       "      <td>8100.0</td>\n",
       "      <td>17.088608</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.265823</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>253.224755</td>\n",
       "      <td>476.393202</td>\n",
       "      <td>170.778057</td>\n",
       "      <td>52.913259</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>4.333333</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>66.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1992</th>\n",
       "      <td>70.0</td>\n",
       "      <td>14.767932</td>\n",
       "      <td>12</td>\n",
       "      <td>2.531646</td>\n",
       "      <td>7.805907</td>\n",
       "      <td>8600.0</td>\n",
       "      <td>18.143460</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.265823</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>223.606798</td>\n",
       "      <td>500.000000</td>\n",
       "      <td>175.059707</td>\n",
       "      <td>55.772782</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>4.333333</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>66.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2004</th>\n",
       "      <td>77.0</td>\n",
       "      <td>16.244726</td>\n",
       "      <td>14</td>\n",
       "      <td>2.953586</td>\n",
       "      <td>8.016878</td>\n",
       "      <td>9000.0</td>\n",
       "      <td>18.987342</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.265823</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>253.224755</td>\n",
       "      <td>1052.996409</td>\n",
       "      <td>342.894544</td>\n",
       "      <td>79.355658</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>4.333333</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>66.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013</th>\n",
       "      <td>78.0</td>\n",
       "      <td>16.455696</td>\n",
       "      <td>15</td>\n",
       "      <td>3.164557</td>\n",
       "      <td>8.016878</td>\n",
       "      <td>9300.0</td>\n",
       "      <td>19.620253</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.265823</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>282.842712</td>\n",
       "      <td>1052.996409</td>\n",
       "      <td>331.304373</td>\n",
       "      <td>76.908158</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>4.333333</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>66.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"12\" valign=\"top\">4</th>\n",
       "      <th rowspan=\"4\" valign=\"top\">&lt;1000</th>\n",
       "      <th>1980</th>\n",
       "      <td>140.0</td>\n",
       "      <td>1.723077</td>\n",
       "      <td>85</td>\n",
       "      <td>1.046154</td>\n",
       "      <td>0.246154</td>\n",
       "      <td>44300.0</td>\n",
       "      <td>5.452308</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.036923</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>424.264069</td>\n",
       "      <td>1526.267650</td>\n",
       "      <td>297.947695</td>\n",
       "      <td>61.555500</td>\n",
       "      <td>1.500000</td>\n",
       "      <td>1.666667</td>\n",
       "      <td>1.5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>33.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1992</th>\n",
       "      <td>129.0</td>\n",
       "      <td>1.587692</td>\n",
       "      <td>82</td>\n",
       "      <td>1.009231</td>\n",
       "      <td>0.258462</td>\n",
       "      <td>40800.0</td>\n",
       "      <td>5.021538</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.036923</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>447.213595</td>\n",
       "      <td>1526.267650</td>\n",
       "      <td>309.285314</td>\n",
       "      <td>60.072469</td>\n",
       "      <td>1.500000</td>\n",
       "      <td>1.666667</td>\n",
       "      <td>1.5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>33.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2004</th>\n",
       "      <td>125.0</td>\n",
       "      <td>1.538462</td>\n",
       "      <td>82</td>\n",
       "      <td>1.009231</td>\n",
       "      <td>0.283077</td>\n",
       "      <td>38800.0</td>\n",
       "      <td>4.775385</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.049231</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>447.213595</td>\n",
       "      <td>1355.634919</td>\n",
       "      <td>314.897465</td>\n",
       "      <td>57.871144</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013</th>\n",
       "      <td>122.0</td>\n",
       "      <td>1.501538</td>\n",
       "      <td>79</td>\n",
       "      <td>0.972308</td>\n",
       "      <td>0.283077</td>\n",
       "      <td>37800.0</td>\n",
       "      <td>4.652308</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.049231</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>447.213595</td>\n",
       "      <td>1624.828759</td>\n",
       "      <td>318.697963</td>\n",
       "      <td>58.713471</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">1000-1500</th>\n",
       "      <th>1980</th>\n",
       "      <td>90.0</td>\n",
       "      <td>1.972387</td>\n",
       "      <td>62</td>\n",
       "      <td>1.358755</td>\n",
       "      <td>0.241069</td>\n",
       "      <td>29600.0</td>\n",
       "      <td>6.486960</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>360.555128</td>\n",
       "      <td>970.469991</td>\n",
       "      <td>245.635053</td>\n",
       "      <td>58.318585</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1992</th>\n",
       "      <td>70.0</td>\n",
       "      <td>1.534078</td>\n",
       "      <td>55</td>\n",
       "      <td>1.205347</td>\n",
       "      <td>0.087662</td>\n",
       "      <td>24100.0</td>\n",
       "      <td>5.281613</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>400.000000</td>\n",
       "      <td>1624.828759</td>\n",
       "      <td>295.113288</td>\n",
       "      <td>64.736316</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2004</th>\n",
       "      <td>62.0</td>\n",
       "      <td>1.358755</td>\n",
       "      <td>45</td>\n",
       "      <td>0.986193</td>\n",
       "      <td>0.065746</td>\n",
       "      <td>20300.0</td>\n",
       "      <td>4.448828</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>400.000000</td>\n",
       "      <td>1624.828759</td>\n",
       "      <td>323.555370</td>\n",
       "      <td>65.744710</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013</th>\n",
       "      <td>63.0</td>\n",
       "      <td>1.380671</td>\n",
       "      <td>43</td>\n",
       "      <td>0.942362</td>\n",
       "      <td>0.065746</td>\n",
       "      <td>21100.0</td>\n",
       "      <td>4.624151</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>447.213595</td>\n",
       "      <td>1569.180601</td>\n",
       "      <td>354.559752</td>\n",
       "      <td>66.120768</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">&gt;1500</th>\n",
       "      <th>1980</th>\n",
       "      <td>161.0</td>\n",
       "      <td>33.966245</td>\n",
       "      <td>7</td>\n",
       "      <td>1.476793</td>\n",
       "      <td>15.400844</td>\n",
       "      <td>12400.0</td>\n",
       "      <td>26.160338</td>\n",
       "      <td>43.0</td>\n",
       "      <td>9.071730</td>\n",
       "      <td>6</td>\n",
       "      <td>...</td>\n",
       "      <td>223.606798</td>\n",
       "      <td>286.295154</td>\n",
       "      <td>100.182435</td>\n",
       "      <td>37.875282</td>\n",
       "      <td>7.166667</td>\n",
       "      <td>11.930233</td>\n",
       "      <td>5.5</td>\n",
       "      <td>18.0</td>\n",
       "      <td>5.842849</td>\n",
       "      <td>81.528131</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1992</th>\n",
       "      <td>162.0</td>\n",
       "      <td>34.177215</td>\n",
       "      <td>8</td>\n",
       "      <td>1.687764</td>\n",
       "      <td>15.822785</td>\n",
       "      <td>12500.0</td>\n",
       "      <td>26.371308</td>\n",
       "      <td>44.0</td>\n",
       "      <td>9.282700</td>\n",
       "      <td>5</td>\n",
       "      <td>...</td>\n",
       "      <td>223.606798</td>\n",
       "      <td>247.213595</td>\n",
       "      <td>76.831993</td>\n",
       "      <td>31.276260</td>\n",
       "      <td>8.800000</td>\n",
       "      <td>12.909091</td>\n",
       "      <td>9.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>6.013319</td>\n",
       "      <td>68.333165</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2004</th>\n",
       "      <td>156.0</td>\n",
       "      <td>32.911392</td>\n",
       "      <td>10</td>\n",
       "      <td>2.109705</td>\n",
       "      <td>15.400844</td>\n",
       "      <td>12100.0</td>\n",
       "      <td>25.527426</td>\n",
       "      <td>44.0</td>\n",
       "      <td>9.282700</td>\n",
       "      <td>6</td>\n",
       "      <td>...</td>\n",
       "      <td>223.606798</td>\n",
       "      <td>247.213595</td>\n",
       "      <td>71.105827</td>\n",
       "      <td>30.062742</td>\n",
       "      <td>7.333333</td>\n",
       "      <td>13.181818</td>\n",
       "      <td>4.5</td>\n",
       "      <td>20.0</td>\n",
       "      <td>6.548961</td>\n",
       "      <td>89.304012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013</th>\n",
       "      <td>157.0</td>\n",
       "      <td>33.122363</td>\n",
       "      <td>11</td>\n",
       "      <td>2.320675</td>\n",
       "      <td>15.611814</td>\n",
       "      <td>12500.0</td>\n",
       "      <td>26.371308</td>\n",
       "      <td>44.0</td>\n",
       "      <td>9.282700</td>\n",
       "      <td>6</td>\n",
       "      <td>...</td>\n",
       "      <td>223.606798</td>\n",
       "      <td>247.213595</td>\n",
       "      <td>74.100090</td>\n",
       "      <td>29.382577</td>\n",
       "      <td>7.333333</td>\n",
       "      <td>13.181818</td>\n",
       "      <td>4.5</td>\n",
       "      <td>20.0</td>\n",
       "      <td>6.548961</td>\n",
       "      <td>89.304012</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>48 rows × 66 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                          total_area  proportion_of_landscape  \\\n",
       "class_val elev-zone date                                        \n",
       "1         <1000     1980       602.0                 7.409231   \n",
       "                    1992       680.0                 8.369231   \n",
       "                    2004       786.0                 9.673846   \n",
       "                    2013       911.0                11.212308   \n",
       "          1000-1500 1980        89.0                 1.950471   \n",
       "                    1992       101.0                 2.213456   \n",
       "                    2004       108.0                 2.366864   \n",
       "                    2013       117.0                 2.564103   \n",
       "          >1500     1980         1.0                 0.210970   \n",
       "                    1992         2.0                 0.421941   \n",
       "                    2004         2.0                 0.421941   \n",
       "                    2013         2.0                 0.421941   \n",
       "2         <1000     1980      6070.0                74.707692   \n",
       "                    1992      5988.0                73.698462   \n",
       "                    2004      5877.0                72.332308   \n",
       "                    2013      5753.0                70.806154   \n",
       "          1000-1500 1980      1915.0                41.968004   \n",
       "                    1992      1840.0                40.324348   \n",
       "                    2004      1818.0                39.842209   \n",
       "                    2013      1800.0                39.447732   \n",
       "          >1500     1980       246.0                51.898734   \n",
       "                    1992       240.0                50.632911   \n",
       "                    2004       239.0                50.421941   \n",
       "                    2013       237.0                50.000000   \n",
       "3         <1000     1980      1313.0                16.160000   \n",
       "                    1992      1328.0                16.344615   \n",
       "                    2004      1337.0                16.455385   \n",
       "                    2013      1339.0                16.480000   \n",
       "          1000-1500 1980      2469.0                54.109139   \n",
       "                    1992      2552.0                55.928117   \n",
       "                    2004      2575.0                56.432172   \n",
       "                    2013      2583.0                56.607495   \n",
       "          >1500     1980        66.0                13.924051   \n",
       "                    1992        70.0                14.767932   \n",
       "                    2004        77.0                16.244726   \n",
       "                    2013        78.0                16.455696   \n",
       "4         <1000     1980       140.0                 1.723077   \n",
       "                    1992       129.0                 1.587692   \n",
       "                    2004       125.0                 1.538462   \n",
       "                    2013       122.0                 1.501538   \n",
       "          1000-1500 1980        90.0                 1.972387   \n",
       "                    1992        70.0                 1.534078   \n",
       "                    2004        62.0                 1.358755   \n",
       "                    2013        63.0                 1.380671   \n",
       "          >1500     1980       161.0                33.966245   \n",
       "                    1992       162.0                34.177215   \n",
       "                    2004       156.0                32.911392   \n",
       "                    2013       157.0                33.122363   \n",
       "\n",
       "                          number_of_patches  patch_density  \\\n",
       "class_val elev-zone date                                     \n",
       "1         <1000     1980                252       3.101538   \n",
       "                    1992                250       3.076923   \n",
       "                    2004                249       3.064615   \n",
       "                    2013                232       2.855385   \n",
       "          1000-1500 1980                 45       0.986193   \n",
       "                    1992                 49       1.073855   \n",
       "                    2004                 47       1.030024   \n",
       "                    2013                 49       1.073855   \n",
       "          >1500     1980                  1       0.210970   \n",
       "                    1992                  2       0.421941   \n",
       "                    2004                  2       0.421941   \n",
       "                    2013                  2       0.421941   \n",
       "2         <1000     1980                 17       0.209231   \n",
       "                    1992                 21       0.258462   \n",
       "                    2004                 22       0.270769   \n",
       "                    2013                 26       0.320000   \n",
       "          1000-1500 1980                 34       0.745124   \n",
       "                    1992                 40       0.876616   \n",
       "                    2004                 42       0.920447   \n",
       "                    2013                 45       0.986193   \n",
       "          >1500     1980                 11       2.320675   \n",
       "                    1992                 11       2.320675   \n",
       "                    2004                 11       2.320675   \n",
       "                    2013                 11       2.320675   \n",
       "3         <1000     1980                152       1.870769   \n",
       "                    1992                151       1.858462   \n",
       "                    2004                150       1.846154   \n",
       "                    2013                151       1.858462   \n",
       "          1000-1500 1980                 46       1.008109   \n",
       "                    1992                 38       0.832785   \n",
       "                    2004                 41       0.898532   \n",
       "                    2013                 41       0.898532   \n",
       "          >1500     1980                 12       2.531646   \n",
       "                    1992                 12       2.531646   \n",
       "                    2004                 14       2.953586   \n",
       "                    2013                 15       3.164557   \n",
       "4         <1000     1980                 85       1.046154   \n",
       "                    1992                 82       1.009231   \n",
       "                    2004                 82       1.009231   \n",
       "                    2013                 79       0.972308   \n",
       "          1000-1500 1980                 62       1.358755   \n",
       "                    1992                 55       1.205347   \n",
       "                    2004                 45       0.986193   \n",
       "                    2013                 43       0.942362   \n",
       "          >1500     1980                  7       1.476793   \n",
       "                    1992                  8       1.687764   \n",
       "                    2004                 10       2.109705   \n",
       "                    2013                 11       2.320675   \n",
       "\n",
       "                          largest_patch_index  total_edge  edge_density  \\\n",
       "class_val elev-zone date                                                  \n",
       "1         <1000     1980             0.750769    176100.0     21.673846   \n",
       "                    1992             0.984615    191300.0     23.544615   \n",
       "                    2004             1.144615    205700.0     25.316923   \n",
       "                    2013             2.289231    219200.0     26.978462   \n",
       "          1000-1500 1980             0.372562     28200.0      6.180145   \n",
       "                    1992             0.372562     31200.0      6.837607   \n",
       "                    2004             0.416393     31000.0      6.793776   \n",
       "                    2013             0.460224     32200.0      7.056761   \n",
       "          >1500     1980             0.210970       100.0      0.210970   \n",
       "                    1992             0.210970       500.0      1.054852   \n",
       "                    2004             0.210970       500.0      1.054852   \n",
       "                    2013             0.210970       500.0      1.054852   \n",
       "2         <1000     1980            71.741538    389800.0     47.975385   \n",
       "                    1992            70.535385    399800.0     49.206154   \n",
       "                    2004            69.304615    412100.0     50.720000   \n",
       "                    2013            67.876923    422100.0     51.950769   \n",
       "          1000-1500 1980            16.392724    186400.0     40.850318   \n",
       "                    1992            12.579443    183000.0     40.105194   \n",
       "                    2004            12.469866    179300.0     39.294324   \n",
       "                    2013            12.447951    178900.0     39.206662   \n",
       "          >1500     1980            18.565401     15800.0     33.333333   \n",
       "                    1992            18.354430     15800.0     33.333333   \n",
       "                    2004            18.143460     15600.0     32.911392   \n",
       "                    2013            17.932489     15900.0     33.544304   \n",
       "3         <1000     1980             2.535385    214400.0     26.387692   \n",
       "                    1992             2.535385    214100.0     26.350769   \n",
       "                    2004             2.535385    212800.0     26.190769   \n",
       "                    2013             2.535385    213500.0     26.276923   \n",
       "          1000-1500 1980            41.200964    193000.0     42.296735   \n",
       "                    1992            46.373000    193500.0     42.406312   \n",
       "                    2004            46.789393    189000.0     41.420118   \n",
       "                    2013            46.833224    188800.0     41.376288   \n",
       "          >1500     1980             7.805907      8100.0     17.088608   \n",
       "                    1992             7.805907      8600.0     18.143460   \n",
       "                    2004             8.016878      9000.0     18.987342   \n",
       "                    2013             8.016878      9300.0     19.620253   \n",
       "4         <1000     1980             0.246154     44300.0      5.452308   \n",
       "                    1992             0.258462     40800.0      5.021538   \n",
       "                    2004             0.283077     38800.0      4.775385   \n",
       "                    2013             0.283077     37800.0      4.652308   \n",
       "          1000-1500 1980             0.241069     29600.0      6.486960   \n",
       "                    1992             0.087662     24100.0      5.281613   \n",
       "                    2004             0.065746     20300.0      4.448828   \n",
       "                    2013             0.065746     21100.0      4.624151   \n",
       "          >1500     1980            15.400844     12400.0     26.160338   \n",
       "                    1992            15.822785     12500.0     26.371308   \n",
       "                    2004            15.400844     12100.0     25.527426   \n",
       "                    2013            15.611814     12500.0     26.371308   \n",
       "\n",
       "                          total_core_area  core_area_proportion_of_landscape  \\\n",
       "class_val elev-zone date                                                       \n",
       "1         <1000     1980             24.0                           0.295385   \n",
       "                    1992             27.0                           0.332308   \n",
       "                    2004             48.0                           0.590769   \n",
       "                    2013             87.0                           1.070769   \n",
       "          1000-1500 1980              1.0                           0.021915   \n",
       "                    1992              1.0                           0.021915   \n",
       "                    2004              2.0                           0.043831   \n",
       "                    2013              2.0                           0.043831   \n",
       "          >1500     1980              0.0                           0.000000   \n",
       "                    1992              0.0                           0.000000   \n",
       "                    2004              0.0                           0.000000   \n",
       "                    2013              0.0                           0.000000   \n",
       "2         <1000     1980           3121.0                          38.412308   \n",
       "                    1992           2996.0                          36.873846   \n",
       "                    2004           2847.0                          35.040000   \n",
       "                    2013           2722.0                          33.501538   \n",
       "          1000-1500 1980            673.0                          14.749069   \n",
       "                    1992            637.0                          13.960114   \n",
       "                    2004            630.0                          13.806706   \n",
       "                    2013            618.0                          13.543721   \n",
       "          >1500     1980             52.0                          10.970464   \n",
       "                    1992             51.0                          10.759494   \n",
       "                    2004             51.0                          10.759494   \n",
       "                    2013             48.0                          10.126582   \n",
       "3         <1000     1980            198.0                           2.436923   \n",
       "                    1992            203.0                           2.498462   \n",
       "                    2004            212.0                           2.609231   \n",
       "                    2013            212.0                           2.609231   \n",
       "          1000-1500 1980           1183.0                          25.925926   \n",
       "                    1992           1244.0                          27.262766   \n",
       "                    2004           1280.0                          28.051720   \n",
       "                    2013           1283.0                          28.117467   \n",
       "          >1500     1980              6.0                           1.265823   \n",
       "                    1992              6.0                           1.265823   \n",
       "                    2004              6.0                           1.265823   \n",
       "                    2013              6.0                           1.265823   \n",
       "4         <1000     1980              3.0                           0.036923   \n",
       "                    1992              3.0                           0.036923   \n",
       "                    2004              4.0                           0.049231   \n",
       "                    2013              4.0                           0.049231   \n",
       "          1000-1500 1980              0.0                           0.000000   \n",
       "                    1992              0.0                           0.000000   \n",
       "                    2004              0.0                           0.000000   \n",
       "                    2013              0.0                           0.000000   \n",
       "          >1500     1980             43.0                           9.071730   \n",
       "                    1992             44.0                           9.282700   \n",
       "                    2004             44.0                           9.282700   \n",
       "                    2013             44.0                           9.282700   \n",
       "\n",
       "                          number_of_disjunct_core_areas  ...  \\\n",
       "class_val elev-zone date                                 ...   \n",
       "1         <1000     1980                              8  ...   \n",
       "                    1992                              9  ...   \n",
       "                    2004                             10  ...   \n",
       "                    2013                             17  ...   \n",
       "          1000-1500 1980                              1  ...   \n",
       "                    1992                              1  ...   \n",
       "                    2004                              2  ...   \n",
       "                    2013                              2  ...   \n",
       "          >1500     1980                              0  ...   \n",
       "                    1992                              0  ...   \n",
       "                    2004                              0  ...   \n",
       "                    2013                              0  ...   \n",
       "2         <1000     1980                             64  ...   \n",
       "                    1992                             72  ...   \n",
       "                    2004                             75  ...   \n",
       "                    2013                             81  ...   \n",
       "          1000-1500 1980                             51  ...   \n",
       "                    1992                             51  ...   \n",
       "                    2004                             56  ...   \n",
       "                    2013                             56  ...   \n",
       "          >1500     1980                              6  ...   \n",
       "                    1992                              6  ...   \n",
       "                    2004                              6  ...   \n",
       "                    2013                              6  ...   \n",
       "3         <1000     1980                             35  ...   \n",
       "                    1992                             35  ...   \n",
       "                    2004                             34  ...   \n",
       "                    2013                             34  ...   \n",
       "          1000-1500 1980                             39  ...   \n",
       "                    1992                             37  ...   \n",
       "                    2004                             38  ...   \n",
       "                    2013                             37  ...   \n",
       "          >1500     1980                              2  ...   \n",
       "                    1992                              2  ...   \n",
       "                    2004                              2  ...   \n",
       "                    2013                              2  ...   \n",
       "4         <1000     1980                              2  ...   \n",
       "                    1992                              2  ...   \n",
       "                    2004                              2  ...   \n",
       "                    2013                              2  ...   \n",
       "          1000-1500 1980                              0  ...   \n",
       "                    1992                              0  ...   \n",
       "                    2004                              0  ...   \n",
       "                    2013                              0  ...   \n",
       "          >1500     1980                              6  ...   \n",
       "                    1992                              5  ...   \n",
       "                    2004                              6  ...   \n",
       "                    2013                              6  ...   \n",
       "\n",
       "                          euclidean_nearest_neighbor_md  \\\n",
       "class_val elev-zone date                                  \n",
       "1         <1000     1980                     223.606798   \n",
       "                    1992                     223.606798   \n",
       "                    2004                     223.606798   \n",
       "                    2013                     223.606798   \n",
       "          1000-1500 1980                     300.000000   \n",
       "                    1992                     300.000000   \n",
       "                    2004                     316.227766   \n",
       "                    2013                     360.555128   \n",
       "          >1500     1980                            NaN   \n",
       "                    1992                    2202.271555   \n",
       "                    2004                    2202.271555   \n",
       "                    2013                    2202.271555   \n",
       "2         <1000     1980                     200.000000   \n",
       "                    1992                     200.000000   \n",
       "                    2004                     200.000000   \n",
       "                    2013                     200.000000   \n",
       "          1000-1500 1980                     200.000000   \n",
       "                    1992                     200.000000   \n",
       "                    2004                     200.000000   \n",
       "                    2013                     200.000000   \n",
       "          >1500     1980                     200.000000   \n",
       "                    1992                     200.000000   \n",
       "                    2004                     200.000000   \n",
       "                    2013                     200.000000   \n",
       "3         <1000     1980                     223.606798   \n",
       "                    1992                     223.606798   \n",
       "                    2004                     223.606798   \n",
       "                    2013                     223.606798   \n",
       "          1000-1500 1980                     200.000000   \n",
       "                    1992                     200.000000   \n",
       "                    2004                     200.000000   \n",
       "                    2013                     223.606798   \n",
       "          >1500     1980                     253.224755   \n",
       "                    1992                     223.606798   \n",
       "                    2004                     253.224755   \n",
       "                    2013                     282.842712   \n",
       "4         <1000     1980                     424.264069   \n",
       "                    1992                     447.213595   \n",
       "                    2004                     447.213595   \n",
       "                    2013                     447.213595   \n",
       "          1000-1500 1980                     360.555128   \n",
       "                    1992                     400.000000   \n",
       "                    2004                     400.000000   \n",
       "                    2013                     447.213595   \n",
       "          >1500     1980                     223.606798   \n",
       "                    1992                     223.606798   \n",
       "                    2004                     223.606798   \n",
       "                    2013                     223.606798   \n",
       "\n",
       "                          euclidean_nearest_neighbor_ra  \\\n",
       "class_val elev-zone date                                  \n",
       "1         <1000     1980                     581.024968   \n",
       "                    1992                     581.024968   \n",
       "                    2004                     581.024968   \n",
       "                    2013                     561.577311   \n",
       "          1000-1500 1980                    2616.025568   \n",
       "                    1992                    2216.609195   \n",
       "                    2004                    2216.609195   \n",
       "                    2013                    2216.609195   \n",
       "          >1500     1980                            NaN   \n",
       "                    1992                       0.000000   \n",
       "                    2004                       0.000000   \n",
       "                    2013                       0.000000   \n",
       "2         <1000     1980                     694.427191   \n",
       "                    1992                     694.427191   \n",
       "                    2004                     694.427191   \n",
       "                    2013                     694.427191   \n",
       "          1000-1500 1980                     470.820393   \n",
       "                    1992                     561.577311   \n",
       "                    2004                     561.577311   \n",
       "                    2013                     561.577311   \n",
       "          >1500     1980                     300.000000   \n",
       "                    1992                     300.000000   \n",
       "                    2004                     300.000000   \n",
       "                    2013                     300.000000   \n",
       "3         <1000     1980                     408.276253   \n",
       "                    1992                     408.276253   \n",
       "                    2004                     528.010989   \n",
       "                    2013                     528.010989   \n",
       "          1000-1500 1980                     160.555128   \n",
       "                    1992                     160.555128   \n",
       "                    2004                     160.555128   \n",
       "                    2013                     160.555128   \n",
       "          >1500     1980                     476.393202   \n",
       "                    1992                     500.000000   \n",
       "                    2004                    1052.996409   \n",
       "                    2013                    1052.996409   \n",
       "4         <1000     1980                    1526.267650   \n",
       "                    1992                    1526.267650   \n",
       "                    2004                    1355.634919   \n",
       "                    2013                    1624.828759   \n",
       "          1000-1500 1980                     970.469991   \n",
       "                    1992                    1624.828759   \n",
       "                    2004                    1624.828759   \n",
       "                    2013                    1569.180601   \n",
       "          >1500     1980                     286.295154   \n",
       "                    1992                     247.213595   \n",
       "                    2004                     247.213595   \n",
       "                    2013                     247.213595   \n",
       "\n",
       "                          euclidean_nearest_neighbor_sd  \\\n",
       "class_val elev-zone date                                  \n",
       "1         <1000     1980                     124.937258   \n",
       "                    1992                     114.225158   \n",
       "                    2004                     111.658845   \n",
       "                    2013                     109.128542   \n",
       "          1000-1500 1980                     501.796831   \n",
       "                    1992                     355.941215   \n",
       "                    2004                     442.227644   \n",
       "                    2013                     435.754483   \n",
       "          >1500     1980                            NaN   \n",
       "                    1992                       0.000000   \n",
       "                    2004                       0.000000   \n",
       "                    2013                       0.000000   \n",
       "2         <1000     1980                     164.098358   \n",
       "                    1992                     148.848153   \n",
       "                    2004                     145.890605   \n",
       "                    2013                     135.571676   \n",
       "          1000-1500 1980                      84.358395   \n",
       "                    1992                      92.925495   \n",
       "                    2004                      89.403555   \n",
       "                    2013                      87.023996   \n",
       "          >1500     1980                      99.295132   \n",
       "                    1992                      99.295132   \n",
       "                    2004                      99.295132   \n",
       "                    2013                     105.269873   \n",
       "3         <1000     1980                      93.084816   \n",
       "                    1992                      91.579955   \n",
       "                    2004                      95.684598   \n",
       "                    2013                      95.872336   \n",
       "          1000-1500 1980                      42.456789   \n",
       "                    1992                      45.191377   \n",
       "                    2004                      44.190577   \n",
       "                    2013                      44.752789   \n",
       "          >1500     1980                     170.778057   \n",
       "                    1992                     175.059707   \n",
       "                    2004                     342.894544   \n",
       "                    2013                     331.304373   \n",
       "4         <1000     1980                     297.947695   \n",
       "                    1992                     309.285314   \n",
       "                    2004                     314.897465   \n",
       "                    2013                     318.697963   \n",
       "          1000-1500 1980                     245.635053   \n",
       "                    1992                     295.113288   \n",
       "                    2004                     323.555370   \n",
       "                    2013                     354.559752   \n",
       "          >1500     1980                     100.182435   \n",
       "                    1992                      76.831993   \n",
       "                    2004                      71.105827   \n",
       "                    2013                      74.100090   \n",
       "\n",
       "                          euclidean_nearest_neighbor_cv  \\\n",
       "class_val elev-zone date                                  \n",
       "1         <1000     1980                      43.480370   \n",
       "                    1992                      41.006394   \n",
       "                    2004                      40.706115   \n",
       "                    2013                      39.692040   \n",
       "          1000-1500 1980                     103.886774   \n",
       "                    1992                      85.601955   \n",
       "                    2004                      93.480134   \n",
       "                    2013                      88.662791   \n",
       "          >1500     1980                            NaN   \n",
       "                    1992                       0.000000   \n",
       "                    2004                       0.000000   \n",
       "                    2013                       0.000000   \n",
       "2         <1000     1980                      63.032003   \n",
       "                    1992                      58.176098   \n",
       "                    2004                      57.591673   \n",
       "                    2013                      55.309188   \n",
       "          1000-1500 1980                      34.974357   \n",
       "                    1992                      38.382304   \n",
       "                    2004                      37.602106   \n",
       "                    2013                      37.075659   \n",
       "          >1500     1980                      35.687854   \n",
       "                    1992                      35.687854   \n",
       "                    2004                      35.687854   \n",
       "                    2013                      36.638140   \n",
       "3         <1000     1980                      34.090164   \n",
       "                    1992                      33.516447   \n",
       "                    2004                      35.227288   \n",
       "                    2013                      34.919733   \n",
       "          1000-1500 1980                      18.716277   \n",
       "                    1992                      19.686685   \n",
       "                    2004                      18.948529   \n",
       "                    2013                      18.862749   \n",
       "          >1500     1980                      52.913259   \n",
       "                    1992                      55.772782   \n",
       "                    2004                      79.355658   \n",
       "                    2013                      76.908158   \n",
       "4         <1000     1980                      61.555500   \n",
       "                    1992                      60.072469   \n",
       "                    2004                      57.871144   \n",
       "                    2013                      58.713471   \n",
       "          1000-1500 1980                      58.318585   \n",
       "                    1992                      64.736316   \n",
       "                    2004                      65.744710   \n",
       "                    2013                      66.120768   \n",
       "          >1500     1980                      37.875282   \n",
       "                    1992                      31.276260   \n",
       "                    2004                      30.062742   \n",
       "                    2013                      29.382577   \n",
       "\n",
       "                          disjunct_core_area_mn  disjunct_core_area_am  \\\n",
       "class_val elev-zone date                                                 \n",
       "1         <1000     1980               3.000000               5.750000   \n",
       "                    1992               3.000000               8.259259   \n",
       "                    2004               4.800000              11.125000   \n",
       "                    2013               5.117647              21.068966   \n",
       "          1000-1500 1980               1.000000               1.000000   \n",
       "                    1992               1.000000               1.000000   \n",
       "                    2004               1.000000               1.000000   \n",
       "                    2013               1.000000               1.000000   \n",
       "          >1500     1980                    NaN                    NaN   \n",
       "                    1992                    NaN                    NaN   \n",
       "                    2004                    NaN                    NaN   \n",
       "                    2013                    NaN                    NaN   \n",
       "2         <1000     1980              48.765625             905.413970   \n",
       "                    1992              41.611111             927.955941   \n",
       "                    2004              37.960000            1462.589041   \n",
       "                    2013              33.604938             935.440852   \n",
       "          1000-1500 1980              13.196078              69.430906   \n",
       "                    1992              12.490196              48.864992   \n",
       "                    2004              11.250000              46.771429   \n",
       "                    2013              11.035714              46.148867   \n",
       "          >1500     1980               8.666667              13.615385   \n",
       "                    1992               8.500000              13.235294   \n",
       "                    2004               8.500000              13.235294   \n",
       "                    2013               8.000000              12.500000   \n",
       "3         <1000     1980               5.657143              24.606061   \n",
       "                    1992               5.800000              24.201970   \n",
       "                    2004               6.235294              24.754717   \n",
       "                    2013               6.235294              24.688679   \n",
       "          1000-1500 1980              30.333333             202.391378   \n",
       "                    1992              33.621622             220.766881   \n",
       "                    2004              33.684211             226.190625   \n",
       "                    2013              34.675676             226.632112   \n",
       "          >1500     1980               3.000000               4.333333   \n",
       "                    1992               3.000000               4.333333   \n",
       "                    2004               3.000000               4.333333   \n",
       "                    2013               3.000000               4.333333   \n",
       "4         <1000     1980               1.500000               1.666667   \n",
       "                    1992               1.500000               1.666667   \n",
       "                    2004               2.000000               2.000000   \n",
       "                    2013               2.000000               2.000000   \n",
       "          1000-1500 1980                    NaN                    NaN   \n",
       "                    1992                    NaN                    NaN   \n",
       "                    2004                    NaN                    NaN   \n",
       "                    2013                    NaN                    NaN   \n",
       "          >1500     1980               7.166667              11.930233   \n",
       "                    1992               8.800000              12.909091   \n",
       "                    2004               7.333333              13.181818   \n",
       "                    2013               7.333333              13.181818   \n",
       "\n",
       "                          disjunct_core_area_md  disjunct_core_area_ra  \\\n",
       "class_val elev-zone date                                                 \n",
       "1         <1000     1980                    2.0                    9.0   \n",
       "                    1992                    1.0                   13.0   \n",
       "                    2004                    2.5                   16.0   \n",
       "                    2013                    1.0                   31.0   \n",
       "          1000-1500 1980                    1.0                    0.0   \n",
       "                    1992                    1.0                    0.0   \n",
       "                    2004                    1.0                    0.0   \n",
       "                    2013                    1.0                    0.0   \n",
       "          >1500     1980                    NaN                    NaN   \n",
       "                    1992                    NaN                    NaN   \n",
       "                    2004                    NaN                    NaN   \n",
       "                    2013                    NaN                    NaN   \n",
       "2         <1000     1980                    2.0                 1322.0   \n",
       "                    1992                    2.0                 1417.0   \n",
       "                    2004                    2.0                 2017.0   \n",
       "                    2013                    2.0                 1525.0   \n",
       "          1000-1500 1980                    3.0                  184.0   \n",
       "                    1992                    4.0                  132.0   \n",
       "                    2004                    3.5                  130.0   \n",
       "                    2013                    3.5                  128.0   \n",
       "          >1500     1980                    9.5                   16.0   \n",
       "                    1992                    9.5                   15.0   \n",
       "                    2004                    9.5                   15.0   \n",
       "                    2013                    8.0                   15.0   \n",
       "3         <1000     1980                    3.0                   62.0   \n",
       "                    1992                    3.0                   62.0   \n",
       "                    2004                    3.0                   62.0   \n",
       "                    2013                    3.0                   62.0   \n",
       "          1000-1500 1980                    5.0                  354.0   \n",
       "                    1992                    5.0                  378.0   \n",
       "                    2004                    4.0                  387.0   \n",
       "                    2013                    4.0                  387.0   \n",
       "          >1500     1980                    3.0                    4.0   \n",
       "                    1992                    3.0                    4.0   \n",
       "                    2004                    3.0                    4.0   \n",
       "                    2013                    3.0                    4.0   \n",
       "4         <1000     1980                    1.5                    1.0   \n",
       "                    1992                    1.5                    1.0   \n",
       "                    2004                    2.0                    0.0   \n",
       "                    2013                    2.0                    0.0   \n",
       "          1000-1500 1980                    NaN                    NaN   \n",
       "                    1992                    NaN                    NaN   \n",
       "                    2004                    NaN                    NaN   \n",
       "                    2013                    NaN                    NaN   \n",
       "          >1500     1980                    5.5                   18.0   \n",
       "                    1992                    9.0                   18.0   \n",
       "                    2004                    4.5                   20.0   \n",
       "                    2013                    4.5                   20.0   \n",
       "\n",
       "                          disjunct_core_area_sd  disjunct_core_area_cv  \n",
       "class_val elev-zone date                                                \n",
       "1         <1000     1980               2.872281              95.742711  \n",
       "                    1992               3.972125             132.404170  \n",
       "                    2004               5.509991             114.791478  \n",
       "                    2013               9.035110             176.548132  \n",
       "          1000-1500 1980               0.000000               0.000000  \n",
       "                    1992               0.000000               0.000000  \n",
       "                    2004               0.000000               0.000000  \n",
       "                    2013               0.000000               0.000000  \n",
       "          >1500     1980                    NaN                    NaN  \n",
       "                    1992                    NaN                    NaN  \n",
       "                    2004                    NaN                    NaN  \n",
       "                    2013                    NaN                    NaN  \n",
       "2         <1000     1980             204.389315             419.125797  \n",
       "                    1992             192.046331             461.526563  \n",
       "                    2004             232.548744             612.615237  \n",
       "                    2013             174.086588             518.038709  \n",
       "          1000-1500 1980              27.241131             206.433531  \n",
       "                    1992              21.314979             170.653679  \n",
       "                    2004              19.990399             177.692440  \n",
       "                    2013              19.684987             178.375289  \n",
       "          >1500     1980               6.548961              75.564933  \n",
       "                    1992               6.344289              74.638691  \n",
       "                    2004               6.344289              74.638691  \n",
       "                    2013               6.000000              75.000000  \n",
       "3         <1000     1980              10.353586             183.017927  \n",
       "                    1992              10.331090             178.122248  \n",
       "                    2004              10.745885             172.339668  \n",
       "                    2013              10.726709             172.032124  \n",
       "          1000-1500 1980              72.243297             238.164717  \n",
       "                    1992              79.322929             235.928326  \n",
       "                    2004              80.525937             239.061377  \n",
       "                    2013              81.585655             235.282092  \n",
       "          >1500     1980               2.000000              66.666667  \n",
       "                    1992               2.000000              66.666667  \n",
       "                    2004               2.000000              66.666667  \n",
       "                    2013               2.000000              66.666667  \n",
       "4         <1000     1980               0.500000              33.333333  \n",
       "                    1992               0.500000              33.333333  \n",
       "                    2004               0.000000               0.000000  \n",
       "                    2013               0.000000               0.000000  \n",
       "          1000-1500 1980                    NaN                    NaN  \n",
       "                    1992                    NaN                    NaN  \n",
       "                    2004                    NaN                    NaN  \n",
       "                    2013                    NaN                    NaN  \n",
       "          >1500     1980               5.842849              81.528131  \n",
       "                    1992               6.013319              68.333165  \n",
       "                    2004               6.548961              89.304012  \n",
       "                    2013               6.548961              89.304012  \n",
       "\n",
       "[48 rows x 66 columns]"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stza.compute_class_metrics_df()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "dd443a61",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[########################################] | 100% Completed | 106.25 ms\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Axes: ylabel='PLAND'>"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "stza.plot_metric(\"proportion_of_landscape\", class_val=URBAN_CLASS_VAL)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python (pylandstats)",
   "language": "python",
   "name": "pylandstats"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}