notebooks/01-landscape-analysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "2e22f0e6",
   "metadata": {},
   "source": [
    "# Landscape analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7abfc3d0",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib as mpl\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import swisslandstats as sls\n",
    "\n",
    "import pylandstats as pls"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8d5bc14f",
   "metadata": {},
   "source": [
    "Running the cell below is required so that the land use/land cover (LULC) data is downloaded and preprocessed (see [A03-swisslandstats-preprocessing.ipynb](https://github.com/martibosch/pylandstats-notebooks/blob/master/notebooks/A03-swisslandstats-preprocessing.ipynb) for more details)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d706d022",
   "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/2024-07-25T193547.935802.snakemake.log\u001b[0m\r\n"
     ]
    }
   ],
   "source": [
    "!snakemake -c1 -d .. -s ../Snakefile veveyse_lulc_tifs"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "10ca4440",
   "metadata": {},
   "source": [
    "We can load our landscape from a GeoTiff file:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b9ab2848",
   "metadata": {},
   "outputs": [],
   "source": [
    "URBAN_CLASS_VAL = 1\n",
    "AGRICULTURAL_CLASS_VAL = 2\n",
    "input_filepath = \"../data/processed/veveyse/LU18_4.tif\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ce532c7a",
   "metadata": {},
   "outputs": [],
   "source": [
    "ls = pls.Landscape(input_filepath)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fb48e368",
   "metadata": {},
   "source": [
    "The Swiss Land Statistics (SLS) inventory distinguishes 27 land use/land cover classes, however, to simplify, this repository uses the classification aggregated into four main categories, i.e., *urban* (1), *agricultural* (3) *wooded areas* and (4) *unproductive areas*:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "560c9ffe",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "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": [
    "ls.plot_landscape(cmap=sls.noas04_4_cmap, norm=sls.noas04_4_norm, legend=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b29b8669",
   "metadata": {},
   "source": [
    "## Computing metrics\n",
    "\n",
    "The metrics can be computed at the patch, class and landscape level (see [the list of implemented metrics](https://pylandstats.readthedocs.io/en/latest/landscape.html#list-of-implemented-metrics))\n",
    "\n",
    "### Patch-level metrics\n",
    "\n",
    "The metrics can be computed at the patch level, that is, for each patch of the landscape:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6a560295",
   "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",
       "      <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>4</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>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": "markdown",
   "id": "aec7d1cd",
   "metadata": {},
   "source": [
    "We can operate upon `patch_metrics_df` as with any other pandas DataFrame. In this case, there are 206 patches, of which 193 are *urban* and 13 *non-urban*, as noted respectively by the values of 1 and 2 within the `class_val` column:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1b26472b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "class_val\n",
       "1    287\n",
       "3    165\n",
       "4    137\n",
       "2     47\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "patch_metrics_df[\"class_val\"].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c7fdc29d",
   "metadata": {},
   "source": [
    "We might also use methods from other libraries, such as matplotlib or numpy. For instance, in order to explore the size distribution of patches, we can also plot the distribution of the logarithm of `area` for *urban* and *agricultural* classes as follows:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6fc889d8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f63c623c710>"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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LFTpUkrCvWAPRF198phkzpmrMmFEaMWKo/vu/r9a6da/UWKewcKsmTrxRF154rq68cozWrl2t2277pSZNGh9ep/rQy6ZNX+imm8bpwgvP1Z//vEJS3Yd7/va39brllp/poovO06hRP9SkSeP19debJUlr1rysoUPPVnFxcY1trr32St1//9111vHRRx9o8uSbJUk33HCdhg49W0OHnh1Re/fff7euvfZK/f3v63X99T/VD384ROvXv63y8nLNm/eQfvrTH+uii87TT35yqX7zmwdUWtr0YbQu7EkBALjCv//9rXr37qvLLrtCPl8rff75p5o79z7Ztq1LLsnT4cMVmjZtkjIyMjV79r2SpPz8J1RaelCdO3ep0VZVVZXuvXe2rrzyat1000RlZvrr7PPNN1/T3XffoaFDh+muu+5XSopXn332qfbu3avTTz8zqjrOOONMTZs2Q/PmPaRZs+5S166nRtXOvn37tGDBb3X99T/XySe318knt1dFRYUsy9L48RPUpk229uzZrWXLntKsWdO1cOGiqPqJBSEFAOAKI0ZcHP63bdvq27e/9uzZrRdffEGXXJKnV155WUVF+/XYY0vUsWMnSdLpp5+pn/70x7VCSjAY1PjxE3XhhSPq7c+2bf3+9ws0cOBgPfjgI+HlQ4YMjamO9PQMnXpqd0lSjx65OvPMXlG1c/BgQL/97UL16nVWjeXTp98e/ncwGNQpp3TUhAk3aMeO7eratVv0A48CIQUA4AqBQEBPPbVY77xToH379ioUCkmSsrKyJEmbN29Sbu5p4YAiSZ06dVb37rl1tjdkyHkn7G/Hju3as2e3Jk78ZXwKiLM2bdrUCiiStG7dK1qx4ln98587VV5eHl6+c+cOQgoAAE3hgQfu1hdffKb//u8b1L17rtLT07Vy5Z/0l7+8LunI4Y82bbJrbZedna1gMFhjWWpqqtLS0k7YX0lJsSQ59uybNm1yai0rKHhLc+bcpf/8z8s1fvwE+f1ttH//Ps2aNV2VlYcTPkZCCgAg6R0+fFh///u7mjjxl/rJT64KLz/2VOaTTjpJW7Z8VWvbAwcOKDMzs8ayxlyvJSurjSSd8Fom1WfTBINVNZYHAoEG24+1vbpKeOutN/S9752u2267I7zs448/jGos8cDZPQCApFdVVaVQKKSUlJTwsrKyQ1q//u3w4zPP7KWtW7/Rv/61K7xs165/atu2rVH12bVrN518cnutWfNyveu0a9dekvSPf2wLL9u69RsVFe0/YdvVdRw+XNlge4WFDbdX7fDhw/J6U2ose+21dY3atimwJwUAkPQyMjLUs2cvLV/+B7Vp00Yej1fLl/9B6ekZKi4ukiSNHn2pli17SrfdNlU33HCTbNtWfv4TyslpK9OM/G96wzA0ceIU3X33Hbrjjl9p1KjRSknx6csvP9eZZ/bSeeedr+9//yydfHJ7PfroPN100yQdOlSqZ599Wn5/3WcLVevSpZs8Ho9eeeVFeTymvF6vzjyzV53tLV/ecHvVBg4cpHnzHtLSpU/qrLP6aMOGv+nDD9+LuPZ4YU8KACAq3rQMedKzEvblTcuIabx33XW/OnXqrPvvv1u/+91vNHz4RRo16j/Cz7dqlap58/6v/H6/7rnnTj322KO6+upr1alTZ6WnR9f3RRf9SA8++Fvt3btXd911h+6++w599tknOvnkk4+8hl6vHnjgEfl8Ps2ePUPLl/9Bv/zlrcrJaXvCdtu0aaOpU2/TJ598pIkTb9QNN1xXb3uTJ09rsL1qY8b8WFddNVZ//vP/6o47btPu3f/WXXfdH1Xt8WDYTr22cCOFQpaKig7FtU2fz6NKy9CK1zbrwMGmvexzm4xWuuKi76mVaciyEv+j8HpNZWen68CBQwoGrYT3n2huqpdak1ei6q2qqtT+/d+qbdtTlJLy3ZVIE35ZfEPfXWHWm9jL4peUFOvKK8fo//yfa/Szn41veIM48HrNFv8+ru+9Uy0nJ10eT8P7STjcAwCIiG3bOlTlkWGkJ6Q/r+e7D227Sk0aUJYv/4NyctqqQ4dTtH//Pv3P/yyXZdkaPfo/m6xP1I+QAgCImG3bCbt/jmXZCdvTbJoeLVv2lPbs2SOPx6Nevc7SwoWPq337DgnpHzURUgAAOOrqq6/V1Vdf29zDwFFMnAUAAI5ESAEAAI5ESAEAnFALPwkUzSBe7xlCCgCgTh6PR5Ka5Z4taNmq3zMeT2xTX5k4CwCok2l6lJaWodLSA5Ikn69Vo+5ZE2+WZSgUcs/enJZcr23bqqw8rNLSA0pLy4jqSr3HIqQAAOrl9x+5U251UGkOpmnKslr2xc0ikQz1pqVlhN87sSCkAADqZRiGsrLaKjMzW6FQMOH9ezyGsrJaq6SkrMXuXYhEMtTr8Xhj3oNSjZACAGiQaZoyzdqXN29qXq+p1NRUlZeHWvyl4hvDbfU2hImzAADAkQgpAADAkQgpAADAkQgpAADAkQgpAADAkQgpAADAkQgpAADAkQgpAADAkQgpAADAkQgpAADAkQgpAADAkQgpAADAkSK6weDatWv18ssv68svv1RJSYm6dOmin/70p7rqqqtOeMfDa6+9Vu+9916t5WvWrFFubm7kowYAAEkvopCydOlSdezYUbfddpvatm2rjRs36v7779fOnTs1Y8aME247YMCAWut07tw58hEDAABXiCikLFq0SDk5OeHHgwcPVllZmZ599llNnTpVPl/9t/H2+/3q169f1AMFAADuEtGclGMDSrWePXvq8OHDKi4ujteYAAAAYp84++GHH6pNmzZq27btCdd777331K9fP/Xu3Vtjx47V+++/H2vXAAAgiUV0uOd4n3/+uV544QVNnDhRHo+n3vUGDhyoMWPG6NRTT9WePXuUn5+vcePG6ZlnnlH//v1jGYIkyeuN70lKpmlKli0ZhgzDiGvbxzOO9uH1mrIsu0n7qovHY9b4nuzcVC+1Ji831eumWiX31dsQw7btqD4Z9+7dqyuvvFLt27fXM888o5SUlEZvW1ZWpry8POXm5urJJ5+Mpvsw27abJEjsPVCuF/66RSWllXFv+1hZGT79ePj31C47rUn7AQCgpYlqT8rBgwd14403KjU1VY8//nhEAUWSWrdurWHDhunVV1+NpvsaLMtWIFAWczvH8nqP7BWqqrJUWRmMa9vHq6r0KBgKqaSkrNn2pPj9aQoEyhUKWQnvP9HcVC+1Ji831eumWiX31Ov3pzVqb1HEIeXw4cO65ZZbtG/fPq1YsULZ2dlRDTDKHTh1Cgbj+4M0TUOSIdl2XMdZF/toH8Gg1SwhpVooZMX9dXQyN9VLrcnLTfW6qVbJffXWJ6KQEgwGNWXKFG3evFnLly9Xp06douq0rKxMBQUF6t27d1TbAwCA5BdRSLn33nv11ltv6Ve/+pUqKir0ySefhJ877bTTlJGRoVmzZmnVqlXatGmTJOmDDz5Qfn6+Ro4cqY4dO2rPnj1aunSp9u7dqwULFsS1GAAAkDwiCinr16+XJP3mN7+p9dyyZcs0aNAgWZalUCgUXt6uXTtVVlZq3rx5Ki4uVlpamvr376977rlHffr0iXH4AAAgWUUUUv7yl780uM7cuXM1d+7c8ONu3bopPz8/8pEBAABX40RsAADgSIQUAADgSIQUAADgSIQUAADgSIQUAADgSIQUAADgSIQUAADgSIQUAADgSIQUAADgSIQUAADgSIQUAADgSIQUAADgSIQUAADgSIQUAADgSIQUAADgSIQUAADgSIQUAADgSIQUAADgSIQUAADgSIQUAADgSIQUAADgSIQUAADgSIQUAADgSIQUAADgSIQUAADgSIQUAADgSIQUAADgSIQUAADgSIQUAADgSIQUAADgSIQUAADgSIQUAADgSIQUAADgSIQUAADgSIQUAADgSIQUAADgSIQUAADgSIQUAADgSIQUAADgSIQUAADgSIQUAADgSIQUAADgSIQUAADgSIQUAADgSIQUAADgSBGFlLVr12rChAkaNmyY+vXrp0svvVR//OMfZVlWg9uuXLlSo0aNUu/evZWXl6e1a9dGPWgAAJD8vJGsvHTpUnXs2FG33Xab2rZtq40bN+r+++/Xzp07NWPGjHq3W7dunWbOnKnx48frvPPO0xtvvKGpU6cqMzNTQ4cOjbkIAACQfCIKKYsWLVJOTk748eDBg1VWVqZnn31WU6dOlc/nq3O7BQsWaNSoUbr11lvD223btk0LFy4kpAAAgDpFdLjn2IBSrWfPnjp8+LCKi4vr3Gbnzp0qLCxUXl5ejeV5eXn67LPPVFRUFMkQAACAS8Q8cfbDDz9UmzZt1LZt2zqfLywslCT16NGjxvLc3FzZth1+HgAA4FgRHe453ueff64XXnhBEydOlMfjqXOdkpISSZLf76+xPCsrq8bzsfB643uSkmmakmVLhiHDMOLa9vGMo314vaYsy27Svuri8Zg1vic7N9VLrcnLTfW6qVbJffU2JOqQsnfvXk2ePFm9e/fWjTfe2OD6x3/Y27Zd5/JImaah7Oz0mNqoS8WBcqWkmPL5YspxDUrxeeT1eJSVldak/TTE72/e/hPNTfVSa/JyU71uqlVyX731ieoT+ODBg7rxxhuVmpqqxx9/XCkpKfWue+wek5NOOim8PBAISKq9hyVSlmUrECiLqY3jeb1H9gpVVVmqrAzGte3jVVV6FAyFVFJS1mx7Uvz+NAUC5QqFGj6VvKVzU73UmrzcVK+bapXcU6/fn9aovUURh5TDhw/rlltu0b59+7RixQplZ2efcP3quSiFhYXKzc0NL9+6dasMw6g1VyUawWB8f5CmaUgyJNsO7/FpKvbRPoJBq1lCSrVQyIr76+hkbqqXWpOXm+p1U62S++qtT0QHvYLBoKZMmaLNmzdryZIl6tSpU4PbdOnSRT169NCaNWtqLF+9erX69OlT5xlDAAAAEe1Juffee/XWW2/pV7/6lSoqKvTJJ5+EnzvttNOUkZGhWbNmadWqVdq0aVP4ucmTJ2vq1Knq2rWrzj33XL355pt69913tWTJkrgVAgAAkktEIWX9+vWSpN/85je1nlu2bJkGDRoky7IUCoVqPHfJJZeooqJCixYtUn5+vrp166b58+dzITcAAFAvw27qSRdNLBSyVFR0KK5t+nweVVqGVry2WQcOVsS17eO1yWilKy76nlqZRrPMSfF6TWVnp+vAgUOuOP7ppnqpNXm5qV431Sq5p96cnPRGTZzlRGwAAOBIhBQAAOBIhBQAAOBIhBQAAOBIhBQAAOBIhBQAAOBIhBQAAOBIhBQAAOBIhBQAAOBIhBQAAOBIhBQAAOBIhBQAAOBIhBQAAOBIhBQAAOBIhBQAAOBIhBQAAOBIhBQAAOBIhBQAAOBIhBQAAOBIhBQAAOBIhBQAAOBIhBQAAOBIhBQAAOBIhBQAAOBIhBQAAOBIhBQAAOBIhBQAAOBIhBQAAOBIhBQAAOBIhBQAAOBIhBQAAOBIhBQAAOBIhBQAAOBIhBQAAOBIhBQAAOBIhBQAAOBIhBQAAOBIhBQAAOBIhBQAAOBIhBQAAOBIhBQAAOBIhBQAAOBIhBQAAOBIhBQAAOBIhBQAAOBI3kg32L59u/Lz8/Xpp59qy5Yt6tGjh1avXt3gdtdee63ee++9WsvXrFmj3NzcSIcBAACSXMQhZcuWLSooKFDfvn1lWZZs2270tgMGDNCMGTNqLOvcuXOkQwAAAC4QcUi58MILNWLECEnSzJkz9cUXXzR6W7/fr379+kXaJQAAcKGI56SYJtNYAABA00to4njvvffUr18/9e7dW2PHjtX777+fyO4BAEALEvHhnmgNHDhQY8aM0amnnqo9e/YoPz9f48aN0zPPPKP+/fvH1LbXG9+sZZqmZNmSYcgwjLi2fTzjaB9erynLavz8nnjxeMwa35Odm+ql1uTlpnrdVKvkvnobkrCQMnny5BqPhw8frry8PD322GN68skno27XNA1lZ6fHOrxaKg6UKyXFlM/XtC9Ris8jr8ejrKy0Ju2nIX5/8/afaG6ql1qTl5vqdVOtkvvqrU/CQsrxWrdurWHDhunVV1+NqR3LshUIlMVpVEd4vR5JUlWVpcrKYFzbPl5VpUfBUEglJWXNtifF709TIFCuUMhKeP+J5qZ6qTV5ualeN9Uquadevz+tUXuLmi2kSIro9OUTCQbj+4M0TUOSIdl23MZYH/toH8Gg1SwhpVooZMX9dXQyN9VLrcnLTfW6qVbJffXWp9kOepWVlamgoEC9e/duriEAAAAHi3hPSnl5uQoKCiRJu3btUmlpqdatWydJOuecc5STk6NZs2Zp1apV2rRpkyTpgw8+UH5+vkaOHKmOHTtqz549Wrp0qfbu3asFCxbEsRwAAJAsIg4p+/fv15QpU2osq368bNkyDRo0SJZlKRQKhZ9v166dKisrNW/ePBUXFystLU39+/fXPffcoz59+sRYAgAASEYRh5TOnTvrq6++OuE6c+fO1dy5c8OPu3Xrpvz8/MhHBwAAXIsTsQEAgCMRUgAAgCMRUgAAgCMRUgAAgCMRUgAAgCMRUgAAgCMRUgAAgCMRUgAAgCMRUgAAgCMRUgAAgCMRUgAAgCMRUgAAgCMRUgAAgCMRUgAAgCMRUgAAgCMRUgAAgCMRUgAAgCMRUgAAgCMRUgAAgCMRUgAAgCMRUgAAgCMRUgAAgCMRUgAAgCMRUgAAgCMRUgAAgCMRUgAAgCMRUgAAgCMRUgAAgCMRUgAAgCMRUgAAgCMRUgAAgCMRUgAAgCMRUgAAgCMRUgAAgCMRUgAAgCMRUgAAgCMRUgAAgCMRUgAAgCMRUgAAgCMRUgAAgCMRUgAAgCMRUgAAgCMRUgAAgCMRUgAAgCMRUgAAgCMRUgAAgCNFHFK2b9+uX//61xozZox69eqlvLy8Rm+7cuVKjRo1Sr1791ZeXp7Wrl0bafcAAMAlIg4pW7ZsUUFBgbp166bc3NxGb7du3TrNnDlTI0eO1JNPPqnBgwdr6tSpWr9+faRDAAAALuCNdIMLL7xQI0aMkCTNnDlTX3zxRaO2W7BggUaNGqVbb71VkjR48GBt27ZNCxcu1NChQyMdBgAASHIRhxTTjHway86dO1VYWKhp06bVWJ6Xl6fbb79dRUVFysnJibjdpmQYUma6T7ZtN2k//nSfPKYh0zRibsu21eTjBQAgUSIOKdEoLCyUJPXo0aPG8tzcXNm2rcLCwphCitcb3/m/Ho8pn12pC3tlKBhqHde2j+f1mMrUIZm2IRkxBgxfqipCXkWSUzwes8b3ZOemeqk1ebmpXjfVKrmv3oYkJKSUlJRIkvx+f43lWVlZNZ6Phmkays5Oj35w9SgvCqj4o9dVFsPYGsPrNWS2zZAvJbY3pCctQ5l9R6hNm6yotvf702Lqv6VxU73UmrzcVK+bapXcV299EhJSqhlGzUMa1Ycmjl8eCcuyFQiUxTSu46WkeOWRVHGwRGXFRXFtu1ZfXlPBVkHZpiLaA3I8T1VIrUMhlZWUybIa35DHY8rvT1MgUK5QyIp+AC2Em+ql1uTlpnrdVKvknnr9/rRG7S1KSEg5do/JSSedFF4eCAQk1d7DEqlgML4/SI/HkufovxMxx8O2bdmWEVNftmXLto+8FpGElGqhkBX319HJ3FQvtSYvN9Xrplol99Vbn4Qc9Kqei1I9N6Xa1q1bZRhGrbkqAAAACQkpXbp0UY8ePbRmzZoay1evXq0+ffo47sweAADQ/CI+3FNeXq6CggJJ0q5du1RaWqp169ZJks455xzl5ORo1qxZWrVqlTZt2hTebvLkyZo6daq6du2qc889V2+++abeffddLVmyJE6lAACAZBJxSNm/f7+mTJlSY1n142XLlmnQoEGyLEuhUKjGOpdccokqKiq0aNEi5efnq1u3bpo/fz4XcgMAAHWKOKR07txZX3311QnXmTt3rubOnVtr+eWXX67LL7880i4BAIALcbUYAADgSIQUAADgSIQUAADgSIQUAADgSIQUAADgSIQUAADgSIQUAADgSIQUAADgSIQUAADgSIQUAADgSIQUAADgSIQUAADgSIQUAADgSIQUAADgSIQUAADgSIQUAADgSIQUAADgSIQUAADgSIQUAADgSIQUAADgSIQUAADgSIQUAADgSIQUAADgSIQUAADgSIQUAADgSIQUAADgSIQUAADgSIQUAADgSIQUAADgSIQUAADgSIQUAADgSIQUAADgSIQUAADgSIQUAADgSIQUAADgSIQUAADgSIQUAADgSIQUAADgSIQUAADgSIQUAADgSIQUAADgSIQUAADgSIQUAADgSN7mHgDczTAMGUbi+jNNI/y9+t+Rsm3Jtu14DgsAUAdCCpqNYRhKTwlJwYrE9RmSqkrK1CoUki/aRlJSdajKQ1ABgCYWcUjZtm2b5syZow8//FBpaWkaPXq0pk+frtTU1BNud+211+q9996rtXzNmjXKzc2NdBhIAoYhKVihwEevK1hempg+TUMpKR5VVYVkW5GHDG9ahvwDRsow0kVGAYCmFVFICQQCuv7669WxY0ctXLhQRUVFevDBB1VcXKxHHnmkwe0HDBigGTNm1FjWuXPnyEaMpBMsL1XoUElC+jJMQx6fV6HKYFQhBQCQOBGFlOeee06BQECrVq1STk6OJMnj8Wj69Om65ZZbGtwj4vf71a9fv6gHCwAA3COis3vefvttDRkyJBxQJOniiy+Wz+dTQUFB3AcHAADcK6I9KVu3btUVV1xRY5nP51PXrl21devWBrd/77331K9fP4VCIfXt21dTpkzRwIEDIxtxEjIMQzIkQ9Gf5nKkDUNejynrBIcxjj8zJR5nu0TLNA3JSmiXAIAWJOI5KX6/v9Zyv9+vkpITzykYOHCgxowZo1NPPVV79uxRfn6+xo0bp2eeeUb9+/ePbNTH8Xrje7kX0/yuPaOJz4/1HA0HJwoWjeH1pcrj9cpXdVAnask0DMk4Zo2QcfRsF0utjMTO0TAMU4ZxJKxYCQpI1T9PwzCiukqQYR45ZbqhMOgEHo9Z43syc1OtkrvqdVOtkvvqbUhcTkG2bbvBD/PJkyfXeDx8+HDl5eXpscce05NPPhl136ZpKDs7Pert61NelJi9Cx6PqZBla09RmYKh6HcrZKbkyF9Rru3rX1X5weK6+zJNtctuLV+KM978Kdnt1fq0s+VN8cj0JfZs+JQUT1TbmSkeeTweZWW1jvOImo7fn9bcQ0gYN9UquateN9Uqua/e+kT0yeD3+xUIBGotP3jwYMSnEbdu3VrDhg3Tq6++GtF2x7MsW4FAWUxtHC8lxSvP0bab+q9l+8jxF1VWhVQVjD6kpFbZsm1bh0oOqLSoqM51UrymslMqZZsKnz5rGMeckpvgc2otb2u1tm0FgyEFK4MJ6TPWej0pIYVCIZWVlLWIPSl+f5oCgXKFYgjALYGbapXcVa+bapXcU6/fn9aovUURhZTc3Nxac08qKyu1Y8eOWnNVGiNeH4rBGD7c6+LxWKr+O7vJP7iPaT6WvuxjGjpRO7Zty7aM79Yxj12e2A9d2z4yattS4vqOsV7bsmXbR95zTg8p1UIhK+7/R5zKTbVK7qrXTbVK7qu3PhHt97/gggu0YcMGHThwILzs9ddfV2VlpYYNGxZRx2VlZSooKFDv3r0j2g4AALhDRCHlqquuUmZmpiZMmKB33nlHq1at0n333adLL720xuGeWbNmqVevXuHHH3zwgW655Ra98MIL2rBhg1566SVdc8012rt3ryZOnBi/agAAQNKIeE7K008/rTlz5ugXv/iFUlNTlZeXp+nTp9dYz7IshUKh8ON27dqpsrJS8+bNU3FxsdLS0tS/f3/dc8896tOnT3wqAQAASSXiUyq6d++u/Pz8E64zd+5czZ07N/y4W7duDW4DAABwLGeciwoAAHAcQgoAAHAkQgoAAHAkQgoAAHAkQgoAAHAkQgoAAHAkQgoAAHAkQgoAAHAkQgoAAHAkQgoAAHAkQgoAAHAkQgoAAHAkQgoAAHAkQgoAAHAkQgoAAHAkQgoAAHAkb3MPAIllGIZkSIaMo4/13ffqB4kai6o7PzquGNiSZNsxjwkA4ByEFBfxmIYMw5BlWar+ODcsQ1ZlSLYt2XH8kDcMQ6ZpKBSy6l2nehS2LYVi7NtjmgQVAEgyhBQXMY8GlH/vL1PwmPBgmoYsK74f7qk+j05qk1arr2NlesvkD1naW1Sm0uLSqPvyekx1aNtaHsMQEQUAkgchxYWCIUuVVSFJ3+3xsCw7rntSUjxmrb5qj8OWbUtVVv3rAADci4mzAADAkQgpAADAkQgpAADAkQgpAADAkZg4C0TKMGQm+Joy0TBNI/zdMIy4TowGgEQgpAARMH2p8ni9Sq2K/pTpRDFCUlVJmVqFQvKlpOpQlYegAqBFIaQAETC8KbKrKhT4+C0Fyw4293BOyDANpaR4ZHnT5O83QoaRzrXuALQohBQgCqHyUoUOlTT3ME7IMA15fF6FUrgGDYCWiYmzAADAkQgpAADAkQgpAADAkQgpAADAkQgpAADAkQgpAADAkQgpAADAkQgpAADAkQgpAADAkbjiLOAGLeSmiMezbXG/IcDFCClAkjNTWs5NEWvhxoiAqxFSkBQMw5AMydCJ9xZU70wwjGMeRNJPdfvG0T4TwJYUy50BW9JNEY/lTcuQf8BIbowIuBghBS2exzRkGIYsy1JDn2WGZciqDEV9GMHWkaBi21IoQZ+cHtOMOahILeOmiABwLEIKWjzzaED59/4yBUNWw+ubhiwrug/8tqkVypat/SXlCuxv+sMnXo+pDm1by2MYDQYwAEg2hBQkjWDIUmVV6ITrGIYRDilR7Uk5Gm6skN1gXwCA2BBSADhXI89KMk0j/L3638nMTfXWV2tLPPPLMIwGp8I57Wfb3K8zIQVwuMZOCq69Xc3vjZnsG4+5L/Fi+hp/VpIRkqpKytQqFJIvAWOrfyCGPCk+hSoPN2038aw3QWOOVr21trAzvwzDUHpKSApWnHg9p7yXqzXz6xxxSNm2bZvmzJmjDz/8UGlpaRo9erSmT5+u1NTUBrdduXKlFi9erF27dqlbt26aOHGiLrnkkqgGDrhBJJOCj1c9Sdh3dJpOYyb7xmuSbjxEclaSYRpKSfGoqioUPiTXHHzZ7eXvNajJz6SKZ72JGnO06qq1JZ75ZRiSghUKfPS6guX1B2+nvJclZ7zOEYWUQCCg66+/Xh07dtTChQtVVFSkBx98UMXFxXrkkUdOuO26des0c+ZMjR8/Xuedd57eeOMNTZ06VZmZmRo6dGhMRQDJKtJJwbW2Nw2lm4eUGbK0t6hMpcX1/3J06iTdxpyVZJiGPD6vQpXBZv3FHmqdeeR7E59JFc96EzXmaDnlZxsvwQZe52SrN1YRhZTnnntOgUBAq1atUk5OjiTJ4/Fo+vTpuuWWW5Sbm1vvtgsWLNCoUaN06623SpIGDx6sbdu2aeHChYQUoAGNmRR8vOpJwqHQkR0jVVbkbQBAc4ro3j1vv/22hgwZEg4oknTxxRfL5/OpoKCg3u127typwsJC5eXl1Viel5enzz77TEVFRREOG0BTCM9/MYzm/zruwnkn/qoefzOPPcJ5QwBOLKI9KVu3btUVV1xRY5nP51PXrl21devWercrLCyUJPXo0aPG8tzcXNm2rcLCwhrBB0DixTL/JVLf7eWp/xBW9SgaM5fmRBfpa0xf8WIdvdwfWQWIj4jnpPj9/lrL/X6/SkrqP8ZW/dzx22ZlZdV4PhqmaSgnJz3q7etiGIZk+9T3yhtlWU37i83UkV+gp1i27Bg+GkyPV95WqSccc319GVLcP5QaU1djxhyvvo4VS72eo2Pu9ZMbZFlNf+gk1veHIclo5Otc3ZdlxfJObOS4jOr5NvX3ZXo88rRKVbsLr5Ydw/ujMX3Fi+nxyNsqVe0vuiamMTfIOOZ9HGNRhscjM6WV2o+42plzIOqq1TRk+lqrVUu7aaadqvYjr5FO9DrH8WcbM9OQ6UtTK8OM+8TZxp5eHZdTkG3bbtR9TI5fp/ovnljugWIYhjyepnijeuRLrx3Imkq8zgVvzJgTed55Y/qK1+ucyLp86ZkJ7C0+tSXy/RxPiX6t48GTltHcQ4iYJ63lvc4tjymT1zkiEc1J8fv9CgQCtZYfPHiwzj0s1erbY1Ld1om2BQAA7hRRSMnNza0196SyslI7duw44Zk91XNRquemVNu6dasMw6g1VwUAACCikHLBBRdow4YNOnDgQHjZ66+/rsrKSg0bNqze7bp06aIePXpozZo1NZavXr1affr0YdIsAACoJaKQctVVVykzM1MTJkzQO++8o1WrVum+++7TpZdeWmNPyqxZs9SrV68a206ePFlr167V/PnztXHjRj3wwAN69913NXny5PhUAgAAkkpE8/H8fr+efvppzZkzR7/4xS+UmpqqvLw8TZ8+vcZ6lmUpFKp55sMll1yiiooKLVq0SPn5+erWrZvmz5/PhdwAAECdDLul3J0JAAC4SkSHewAAABKFkAIAAByJkAIAAByJkAIAAByJkAIAAByJkAIAABwpqULK9u3b9etf/1pjxoxRr169lJeXV+d6BQUFuuyyy9S7d2+NHDlSzz777AnbLSsr0+OPP97geqWlpfr1r3+tQYMGqX///rr55pu1a9euqOs5EWqtrSlqrays1MMPP6xrrrlG/fr10xlnnKGioqKY6mmIm+ptzlo/++wzzZo1Sz/60Y/Ut29fjRw5UnPnzlVpaWlMNZ2Im+ptzlp37dqlm2++WRdccIF69+6toUOHavLkydq2bVtMNdXHTbUmWiJvHNvktmzZooKCAvXt21eWZamuS8B8/PHHmjBhgsaMGaOZM2fqo48+0pw5c+Tz+fRf//VftdYPBoP65S9/qQ0bNqiyslK7d+/WtGnT6uz/1ltv1ZdffqnZs2crIyNDCxcu1Lhx4/TSSy8pNTWVWltgrRUVFXr++efVu3dv/eAHP9D69evjWltd3FRvc9a6du1abdu2TT/72c/UvXt3/eMf/9CCBQv00Ucf6bnnnpNpxv9vODfV25y1lpWVqV27dvrVr36l9u3ba8+ePVq8eLGuu+46vfjii3G/FYubak04O4mEQqHwv2fMmGGPHj261jo///nP7Z/85Cc1lt155532eeedV2P7ajNnzrT79etnb9y40V6xYoV95pln2suWLau13ieffGKffvrp9l//+tfwsl27dtm9evWy//jHP8ZSVp2otaamqtW2bduyLNu2bfvPf/6zffrpp9v79++PpZwGuane5qy1rrreeOMN+/TTT7c3btwYTTkNclO9zf0+Pt62bdvs008/3X7ppZcirKRhbqo10ZLqcE9DfwlUVlZqw4YNGj16dI3ll156qfbu3atNmzbVWP7b3/5Wr732mvLz83XOOefoyiuv1IMPPqiHHnqo1s0SCwoK5Pf7dcEFF4SXdezYUQMGDFBBQUGMldVGrd9pylolyTCM2IuIgJvqbc5a6/oLs/qeY3v27ImmnAa5qd7mfh8fr02bNpKO7KGINzfVmmhJdbinITt27FBVVZV69OhRY/lpp50mSdq6davOOussSdLy5cu1YsUKLV26VH369Amve9lllyklJUV33HGHsrOzNWTIkPC23bt3r/UL/rTTTkvIIYLjUWt8anUiN9Wb6Fo/+OADSapxw9REclO9iai1+j5yu3fv1vz583XKKadoxIgRTVxZbW6qNd5cFVJKSkokHblR4rGqH1c/L0ljx47V2LFj62xn9OjRtRJxIBBQZmZmrXX9fn+NdhOFWuNTqxO5qd5E1hoIBDR//nwNGTJEPXv2jGXYUXNTvYmo9bbbbtPLL78sSeratauWLl1a5++upuamWuMtqQ73NFZ9u7Nj3c3dVO3Gglqbd0xNyU31NnWtoVBI06dPV3l5ue6///64tBkLN9XblLVOmTJFzz//vBYuXKiTTjpJ48aN07/+9a+Y242Wm2qNF1eFlKysLEmq9dd+IBCQVDvlRsLv94fbOb7tWNqNFrXGp1YnclO9iap19uzZev/997V48WJ16tQpLm1Gw031JqLWLl26qE+fPrr44ouVn5+vqqoqLVmyJOZ2I+WmWuPNVSGla9euSklJUWFhYY3l33zzjaTYjsvm5uZq27ZttU49++abb5rleC+1xqdWJ3JTvYmo9eGHH9aLL76o3/3udzXmADQHN9Wb6Pdx69at1aNHD23fvj2u7TaGm2qNN1eFFJ/Pp8GDB2vt2rU1lq9evVrt2rULz3SPxrBhwxQIBPTOO++El3377bf66KOPNGzYsKjbjRa1xqdWJ3JTvU1d65NPPqmnnnpKc+bMaZb37vHcVG+i38eBQEBff/21unTpEtd2G8NNtcZbUk2cLS8vD58Cu2vXLpWWlmrdunWSpHPOOUc5OTmaOHGixo4dqzvvvFOXXnqpPvroIz3//PO69957Y7qYUd++fTV8+HDdcccdmjlzpjIyMrRgwQJ16tRJl19+eVzqOxa1JqZW6cgp1+Xl5friiy8kSW+99ZbS09N12mmnhWfnx5Ob6m3OWl9++WU98sgjGj16tLp3765PPvkk/FyHDh3UoUOHmGqri5vqbc5aH330UR08eFADBgxQTk6Odu3apaefflrBYFDXX399XOo7lptqTTTDPn6ffQv2z3/+UxdddFGdzy1btkyDBg2SdOSX8Lx587R161Z16NBB48aN0zXXXBNz/6WlpXrooYf06quvqqqqSoMGDdLs2bOb5JgvtR6RiFovvPDCOi/5P2nSJP3iF7+Iuf3juane5qx15syZWrlyZZ3PJePPNtH1Nmetb775pv7whz/o66+/VllZmdq3b6+BAwdqwoQJTbJ3wU21JlpShRQAAJA8XDUnBQAAtByEFAAA4EiEFAAA4EiEFAAA4EiEFAAA4EiEFAAA4EiEFAAA4EiEFAAA4EiEFAAA4EiEFAAA4EiEFAAA4EiEFAAA4Ej/HyxWWvllhrsDAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# for better-looking histograms\n",
    "sns.set_theme()\n",
    "\n",
    "ax = (\n",
    "    patch_metrics_df[patch_metrics_df[\"class_val\"] == URBAN_CLASS_VAL]\n",
    "    .apply(np.log10)\n",
    "    .hist(column=\"area\", label=\"urban\", density=True, alpha=0.66)\n",
    ")\n",
    "patch_metrics_df[patch_metrics_df[\"class_val\"] == AGRICULTURAL_CLASS_VAL].apply(\n",
    "    np.log10\n",
    ").hist(column=\"area\", ax=ax, label=\"agricultural\", density=True, alpha=0.66)\n",
    "\n",
    "ax.item().get_xaxis().set_major_formatter(\n",
    "    mpl.ticker.FuncFormatter(lambda x, p: \"10^%d\" % x)\n",
    ")\n",
    "ax.item().legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "289ab525",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = (\n",
    "    patch_metrics_df[patch_metrics_df[\"class_val\"] == 1]\n",
    "    .apply(np.log10)\n",
    "    .hist(column=\"area\", label=\"urban\", density=True)\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f205c54d",
   "metadata": {},
   "source": [
    "<a id='class-metrics-df'></a>\n",
    "### Class-level metrics\n",
    "\n",
    "The metrics can also be computed at the class level, that is, aggregating over all patches of a land use/cover class"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "512d58db",
   "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>total_area</th>\n",
       "      <th>proportion_of_landscape</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>core_area_proportion_of_landscape</th>\n",
       "      <th>number_of_disjunct_core_areas</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>class_val</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>1</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>2</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>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",
       "      <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>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",
       "      <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>4 rows × 66 columns</p>\n",
       "</div>"
      ],
      "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",
       "1                           31.0               8.458723             187.971629  \n",
       "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"
    }
   ],
   "source": [
    "class_metrics_df = ls.compute_class_metrics_df()\n",
    "class_metrics_df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f11b772e",
   "metadata": {},
   "source": [
    "### Landscape-level metrics\n",
    "\n",
    "Finally, the metrics can also be computed at the landscape level, that is, aggregating over all patches of the landscape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3cbb9498",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</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>70.45117</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>482.7143</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1 rows × 71 columns</p>\n",
       "</div>"
      ],
      "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",
       "   euclidean_nearest_neighbor_md  euclidean_nearest_neighbor_ra  \\\n",
       "0                     223.606798                    3124.154028   \n",
       "\n",
       "   euclidean_nearest_neighbor_sd  euclidean_nearest_neighbor_cv  \\\n",
       "0                     236.550901                       70.45117   \n",
       "\n",
       "   disjunct_core_area_mn  disjunct_core_area_am  disjunct_core_area_md  \\\n",
       "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": "44b1156c",
   "metadata": {},
   "source": [
    "## Customizing the metrics DataFrames\n",
    "\n",
    "<a id='subset-class-metrics-df'></a>\n",
    "### Selecting the metrics to compute\n",
    "\n",
    "Some metrics can be expensive to compute. If you are only interested in computing a subset of the metrics implemented within pylandstats, you can specify it in each respective method, that is, `Landscape.compute_patch_metrics_df`, `Landscape.compute_patch_metrics_df` and/or `Landscape.compute_class_metrics_df` through the `metrics` argument (see [the documentation on \"Computing metrics data frames\"](https://pylandstats.readthedocs.io/en/latest/landscape.html#computing-metrics-data-frames). For instance:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4f59c9ec",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "        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>proportion_of_landscape</th>\n",
       "      <th>edge_density</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>class_val</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>7.749572</td>\n",
       "      <td>19.102211</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>58.862503</td>\n",
       "      <td>47.606640</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>30.715402</td>\n",
       "      <td>32.353160</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2.672523</td>\n",
       "      <td>5.650264</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           proportion_of_landscape  edge_density\n",
       "class_val                                       \n",
       "1                         7.749572     19.102211\n",
       "2                        58.862503     47.606640\n",
       "3                        30.715402     32.353160\n",
       "4                         2.672523      5.650264"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "subset_class_metrics_df = ls.compute_class_metrics_df(\n",
    "    metrics=[\"proportion_of_landscape\", \"edge_density\"]\n",
    ")\n",
    "subset_class_metrics_df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c53b61a1",
   "metadata": {},
   "source": [
    "### Customizing how each metric is computed\n",
    "\n",
    "The default arguments correspond to how the metrics are defined within FRAGSTATS. Nevertheless, some metrics allow some variations in their definition. For instance, the `edge_density` above allows us to choose whether we consider the landscape boundary to be an edge (by default, as in FRAGSTATS, we do not, since we only consider edges between land use/cover classes), or whether we want the area to be converted to hectares (by default, as in FRAGSTATS, we do)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "513caa9a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Edge density (without boundary, meters of edge per hectare):\n",
      "52.35613786942604\n",
      "\n",
      "Edge density (with boundary, meters of edge per hectare):\n",
      "59.681381672001784\n",
      "\n",
      "Edge density (with boundary, meters of edge per square meter):\n",
      "0.005968138167200179\n"
     ]
    }
   ],
   "source": [
    "print(\n",
    "    \"Edge density (without boundary, meters of edge per hectare):\\n{}\\n\".format(\n",
    "        ls.edge_density()\n",
    "    )\n",
    ")\n",
    "\n",
    "print(\n",
    "    \"Edge density (with boundary, meters of edge per hectare):\\n{}\\n\".format(\n",
    "        ls.edge_density(count_boundary=True)\n",
    "    )\n",
    ")\n",
    "\n",
    "print(\n",
    "    \"Edge density (with boundary, meters of edge per square meter):\\n{}\".format(\n",
    "        ls.edge_density(count_boundary=True, hectares=False)\n",
    "    )\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1cd49109",
   "metadata": {},
   "source": [
    "For more details, see the documentation for each metric's method.\n",
    "\n",
    "If we want to obtain a patch, class or landscape-level DataFrame with some customized metrics, instead of manually calling each metric's method with its respective parameters, we can use the `metrics_kwargs` argument of the `Landscape.compute_patch_metrics_df`, `Landscape.compute_patch_metrics_df` and/or `Landscape.compute_class_metrics_df` to set the keyword arguments to be passed to the some metric methods. For instance, if we wanted `proportion_of_landscape` as a fraction instead of a percentage and `edge_density` to include the boundary, we can do it as follows:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e85d0608",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>total_area</th>\n",
       "      <th>proportion_of_landscape</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>core_area_proportion_of_landscape</th>\n",
       "      <th>number_of_disjunct_core_areas</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>class_val</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>1</th>\n",
       "      <td>1041.0</td>\n",
       "      <td>0.077496</td>\n",
       "      <td>287</td>\n",
       "      <td>2.136529</td>\n",
       "      <td>1.421872</td>\n",
       "      <td>256600.0</td>\n",
       "      <td>19.325542</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>2</th>\n",
       "      <td>7907.0</td>\n",
       "      <td>0.588625</td>\n",
       "      <td>47</td>\n",
       "      <td>0.349885</td>\n",
       "      <td>46.862205</td>\n",
       "      <td>639500.0</td>\n",
       "      <td>51.053376</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>3</th>\n",
       "      <td>4126.0</td>\n",
       "      <td>0.307154</td>\n",
       "      <td>165</td>\n",
       "      <td>1.228318</td>\n",
       "      <td>20.605970</td>\n",
       "      <td>434600.0</td>\n",
       "      <td>35.286235</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>4</th>\n",
       "      <td>359.0</td>\n",
       "      <td>0.026725</td>\n",
       "      <td>137</td>\n",
       "      <td>1.019876</td>\n",
       "      <td>0.588104</td>\n",
       "      <td>75900.0</td>\n",
       "      <td>6.372367</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>4 rows × 66 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           total_area  proportion_of_landscape  number_of_patches  \\\n",
       "class_val                                                           \n",
       "1              1041.0                 0.077496                287   \n",
       "2              7907.0                 0.588625                 47   \n",
       "3              4126.0                 0.307154                165   \n",
       "4               359.0                 0.026725                137   \n",
       "\n",
       "           patch_density  largest_patch_index  total_edge  edge_density  \\\n",
       "class_val                                                                 \n",
       "1               2.136529             1.421872    256600.0     19.325542   \n",
       "2               0.349885            46.862205    639500.0     51.053376   \n",
       "3               1.228318            20.605970    434600.0     35.286235   \n",
       "4               1.019876             0.588104     75900.0      6.372367   \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",
       "1                           31.0               8.458723             187.971629  \n",
       "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"
    }
   ],
   "source": [
    "custom_class_metrics_df = ls.compute_class_metrics_df(\n",
    "    metrics_kwargs={\n",
    "        \"proportion_of_landscape\": {\"percent\": False},\n",
    "        \"edge_density\": {\"count_boundary\": True},\n",
    "    }\n",
    ")\n",
    "custom_class_metrics_df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "67e89c4a",
   "metadata": {},
   "source": [
    "Note that the values for `proportion_of_landscape` and `edge_density` are different now than when we computed them with the default arguments for their respective methods (in the [class_metrics_df](#class-metrics-df) and [subset_class_metrics_df](#subset-class-metrics-df) above).\n",
    "\n",
    "Note also that the `custom_class_metrics_df` does not only feature `proportion_of_landscape` and `edge_density` but features all the available metrics instead. This is because the `metrics_kwargs` argument does not imply that only the the metrics defined on it will be computed, only that the metrics defined on it will be computed with the specified arguments.\n",
    "\n",
    "We might choose to only compute a reduced set of metrics, some of which with non-default arguments, by setting both the `metrics` and `metric_kwargs` arguments:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2593674c",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>proportion_of_landscape</th>\n",
       "      <th>edge_density</th>\n",
       "      <th>fractal_dimension_am</th>\n",
       "    </tr>\n",
       "    <tr>\n",
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       "      <th>1</th>\n",
       "      <td>0.077496</td>\n",
       "      <td>19.325542</td>\n",
       "      <td>1.113777</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.588625</td>\n",
       "      <td>51.053376</td>\n",
       "      <td>1.284123</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.307154</td>\n",
       "      <td>35.286235</td>\n",
       "      <td>1.224259</td>\n",
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       "      <th>4</th>\n",
       "      <td>0.026725</td>\n",
       "      <td>6.372367</td>\n",
       "      <td>1.058115</td>\n",
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       "</table>\n",
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      ],
      "text/plain": [
       "           proportion_of_landscape  edge_density  fractal_dimension_am\n",
       "class_val                                                             \n",
       "1                         0.077496     19.325542              1.113777\n",
       "2                         0.588625     51.053376              1.284123\n",
       "3                         0.307154     35.286235              1.224259\n",
       "4                         0.026725      6.372367              1.058115"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "custom_subset_class_metrics_df = ls.compute_class_metrics_df(\n",
    "    metrics=[\"proportion_of_landscape\", \"edge_density\", \"fractal_dimension_am\"],\n",
    "    metrics_kwargs={\n",
    "        \"proportion_of_landscape\": {\"percent\": False},\n",
    "        \"edge_density\": {\"count_boundary\": True},\n",
    "    },\n",
    ")\n",
    "custom_subset_class_metrics_df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6570d146",
   "metadata": {},
   "source": [
    "The same could be done for the `Landscape.compute_patch_metrics_df` or `Landscape.compute_landscape_metrics_df` methods. Check the documentation of each metric's method for more details on how they might be customized through their arguments."
   ]
  }
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