{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "0",
   "metadata": {},
   "source": [
    "# Spatial predictors\n",
    "\n",
    "In the focal site multi-scale study framework <cite data-cite=\"brennen2002focal\"/>, a response variable (e.g., species abundance) is measured at multiple sites and the predictor variables are usually measures of landscape structure (e.g. habitat amount and configuration) computed at multiple spatial extents surrounding each focal site. The utility of such a framework transcends species-landscape relationships - in fact, land use regressions to predict air quality or temperature based on multi-scale spatial predictors fundamentally share the same setup <cite data-cite=\"hoek2008review\" />.\n",
    "\n",
    "A key aspect of focal site multi-scale studies is the selection of spatial predictors, which are ultimately used as explanatory variables to study the effect of spatial pattern on a response variable.\n",
    "\n",
    "## Formal definition\n",
    "\n",
    "Let $\\mathcal{S} = \\{s_1, \\ldots, s_n\\}$ be a set of $n$ sampling sites, each located at geographic coordinates $\\mathbf{p}_i \\in \\mathbb{R}^2$, and let $y_i \\in \\mathbb{R}$ be an environmental response variable (e.g. air temperature, species richness) measured at site $s_i$.\n",
    "\n",
    "For site $s_i$, a **spatial predictor** $k$ at spatial extent (buffer radius) $r$ is a vector summary of the environmental layer $\\mathcal{L}$ (raster or vector dataset) within the neighbourhood $B(\\mathbf{p}_i, r)$:\n",
    "\n",
    "$$\\mathbf{x}_{ikr} = f_k\\bigl(\\mathcal{L}\\big|_{B(\\mathbf{p}_i,\\, r)}\\bigr) \\in \\mathbb{R}^{d_k}$$\n",
    "\n",
    "where $f_k$ is an aggregation function (e.g. mean, sum, a landscape metric, or a learned embedding) and $d_k \\geq 1$ is the output dimension ($d_k = 1$ for scalar summaries, $d_k > 1$ for vector embeddings). Concatenating all $K$ predictors across $R$ spatial extents yields the feature matrix\n",
    "\n",
    "$$\\mathbf{X} \\in \\mathbb{R}^{n \\times D}, \\quad D = \\sum_{k=1}^{K} d_k \\cdot R$$\n",
    "\n",
    "which is subsequently used to fit a model $\\hat{y} = g(\\mathbf{X})$ relating landscape structure to the response variable."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1",
   "metadata": {},
   "outputs": [],
   "source": [
    "import contextily as cx\n",
    "import geopandas as gpd\n",
    "import matplotlib.pyplot as plt\n",
    "import rasterio as rio\n",
    "import seaborn as sns\n",
    "\n",
    "import focalpy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2",
   "metadata": {},
   "outputs": [],
   "source": [
    "stations_filepath = \"data/stations.gpkg\"\n",
    "\n",
    "buildings_filepath = \"data/buildings.gpkg\"\n",
    "tree_canopy_filepath = \"data/tree-canopy.tif\"\n",
    "dem_filepath = \"data/dem.tif\"\n",
    "lulc_filepath = \"data/lulc.tif\"\n",
    "tessera_filepath = \"data/tesserach-zurich.tif\"\n",
    "\n",
    "buffer_dists = [50, 100, 250, 500]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3",
   "metadata": {},
   "outputs": [],
   "source": [
    "stations_gdf = gpd.read_file(stations_filepath).set_index(\"station_id\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4",
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "for buffer_dist, color in zip(buffer_dists, sns.color_palette()):\n",
    "    stations_gdf.buffer(buffer_dist).plot(\n",
    "        facecolor=(0, 0, 0, 0), edgecolor=color, ax=ax\n",
    "    )\n",
    "cx.add_basemap(ax, crs=stations_gdf.crs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5",
   "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>height</th>\n",
       "      <th>area</th>\n",
       "      <th>geometry</th>\n",
       "      <th>volume</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4264</th>\n",
       "      <td>18.670619</td>\n",
       "      <td>10340.626863</td>\n",
       "      <td>POLYGON ((2680693.885 1251498.715, 2680695.967...</td>\n",
       "      <td>193065.902368</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4436</th>\n",
       "      <td>16.988255</td>\n",
       "      <td>5692.331205</td>\n",
       "      <td>POLYGON ((2682125.575 1247611.584, 2682110.688...</td>\n",
       "      <td>96702.775194</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4633</th>\n",
       "      <td>21.344529</td>\n",
       "      <td>3200.633956</td>\n",
       "      <td>POLYGON ((2683816.74 1248231.125, 2683816.581 ...</td>\n",
       "      <td>68316.025064</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7565</th>\n",
       "      <td>23.081217</td>\n",
       "      <td>3083.217221</td>\n",
       "      <td>POLYGON ((2683434.771 1248110.727, 2683434.099...</td>\n",
       "      <td>71164.405863</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57243</th>\n",
       "      <td>15.070181</td>\n",
       "      <td>1032.077993</td>\n",
       "      <td>POLYGON ((2683508.7 1247190.211, 2683519.131 1...</td>\n",
       "      <td>15553.602132</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          height          area  \\\n",
       "id                               \n",
       "4264   18.670619  10340.626863   \n",
       "4436   16.988255   5692.331205   \n",
       "4633   21.344529   3200.633956   \n",
       "7565   23.081217   3083.217221   \n",
       "57243  15.070181   1032.077993   \n",
       "\n",
       "                                                geometry         volume  \n",
       "id                                                                       \n",
       "4264   POLYGON ((2680693.885 1251498.715, 2680695.967...  193065.902368  \n",
       "4436   POLYGON ((2682125.575 1247611.584, 2682110.688...   96702.775194  \n",
       "4633   POLYGON ((2683816.74 1248231.125, 2683816.581 ...   68316.025064  \n",
       "7565   POLYGON ((2683434.771 1248110.727, 2683434.099...   71164.405863  \n",
       "57243  POLYGON ((2683508.7 1247190.211, 2683519.131 1...   15553.602132  "
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# buildings\n",
    "buildings_gdf = gpd.read_file(buildings_filepath).set_index(\"id\")\n",
    "# add a \"volume\" column\n",
    "buildings_gdf[\"volume\"] = buildings_gdf[\"area\"] * buildings_gdf[\"height\"]\n",
    "buildings_gdf.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6",
   "metadata": {},
   "outputs": [],
   "source": [
    "# dem\n",
    "with rio.open(dem_filepath) as src:\n",
    "    # read the one and only band\n",
    "    dem_arr = src.read(1)\n",
    "    dem_transform = src.transform\n",
    "    dem_nodata = src.nodata"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7",
   "metadata": {},
   "source": [
    "## Computing features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>building_volume_sum_50</th>\n",
       "      <th>building_volume_sum_100</th>\n",
       "      <th>building_volume_sum_250</th>\n",
       "      <th>building_volume_sum_500</th>\n",
       "      <th>tree_sum_50</th>\n",
       "      <th>tree_sum_100</th>\n",
       "      <th>tree_sum_250</th>\n",
       "      <th>tree_sum_500</th>\n",
       "      <th>slope_mean_50</th>\n",
       "      <th>slope_mean_100</th>\n",
       "      <th>slope_mean_250</th>\n",
       "      <th>slope_mean_500</th>\n",
       "      <th>topographic_position_index_mean_50</th>\n",
       "      <th>topographic_position_index_mean_100</th>\n",
       "      <th>topographic_position_index_mean_250</th>\n",
       "      <th>topographic_position_index_mean_500</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>station_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",
       "      <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>2651</th>\n",
       "      <td>42799.252900</td>\n",
       "      <td>130087.412717</td>\n",
       "      <td>5.394371e+05</td>\n",
       "      <td>1.874661e+06</td>\n",
       "      <td>650.0</td>\n",
       "      <td>2281.0</td>\n",
       "      <td>12913.0</td>\n",
       "      <td>59364.0</td>\n",
       "      <td>6.258385</td>\n",
       "      <td>5.147757</td>\n",
       "      <td>3.865617</td>\n",
       "      <td>3.948312</td>\n",
       "      <td>0.002324</td>\n",
       "      <td>0.001576</td>\n",
       "      <td>0.000070</td>\n",
       "      <td>-0.000084</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2652</th>\n",
       "      <td>28891.012664</td>\n",
       "      <td>100381.131949</td>\n",
       "      <td>6.930643e+05</td>\n",
       "      <td>2.925047e+06</td>\n",
       "      <td>134.0</td>\n",
       "      <td>1607.0</td>\n",
       "      <td>10679.0</td>\n",
       "      <td>38572.0</td>\n",
       "      <td>3.295830</td>\n",
       "      <td>3.792125</td>\n",
       "      <td>3.354241</td>\n",
       "      <td>3.435203</td>\n",
       "      <td>-0.000874</td>\n",
       "      <td>0.000531</td>\n",
       "      <td>-0.000046</td>\n",
       "      <td>-0.000022</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2653</th>\n",
       "      <td>8218.052929</td>\n",
       "      <td>39102.191933</td>\n",
       "      <td>3.850106e+05</td>\n",
       "      <td>1.081819e+06</td>\n",
       "      <td>845.0</td>\n",
       "      <td>3211.0</td>\n",
       "      <td>18900.0</td>\n",
       "      <td>83168.0</td>\n",
       "      <td>6.053758</td>\n",
       "      <td>5.753644</td>\n",
       "      <td>6.210232</td>\n",
       "      <td>8.461916</td>\n",
       "      <td>0.000385</td>\n",
       "      <td>0.000706</td>\n",
       "      <td>0.000689</td>\n",
       "      <td>0.000174</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2655</th>\n",
       "      <td>69366.625859</td>\n",
       "      <td>330706.307361</td>\n",
       "      <td>1.378667e+06</td>\n",
       "      <td>4.091401e+06</td>\n",
       "      <td>149.0</td>\n",
       "      <td>1005.0</td>\n",
       "      <td>9609.0</td>\n",
       "      <td>33100.0</td>\n",
       "      <td>1.742169</td>\n",
       "      <td>1.645347</td>\n",
       "      <td>2.092990</td>\n",
       "      <td>3.686358</td>\n",
       "      <td>-0.000051</td>\n",
       "      <td>-0.000204</td>\n",
       "      <td>-0.000106</td>\n",
       "      <td>-0.000154</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2656</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>360.659244</td>\n",
       "      <td>8.534503e+03</td>\n",
       "      <td>1.080769e+04</td>\n",
       "      <td>1950.0</td>\n",
       "      <td>7070.0</td>\n",
       "      <td>40955.0</td>\n",
       "      <td>172565.0</td>\n",
       "      <td>27.618801</td>\n",
       "      <td>29.190599</td>\n",
       "      <td>28.890853</td>\n",
       "      <td>29.256348</td>\n",
       "      <td>0.011389</td>\n",
       "      <td>0.003584</td>\n",
       "      <td>0.002101</td>\n",
       "      <td>0.000425</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            building_volume_sum_50  building_volume_sum_100  \\\n",
       "station_id                                                    \n",
       "2651                  42799.252900            130087.412717   \n",
       "2652                  28891.012664            100381.131949   \n",
       "2653                   8218.052929             39102.191933   \n",
       "2655                  69366.625859            330706.307361   \n",
       "2656                      0.000000               360.659244   \n",
       "\n",
       "            building_volume_sum_250  building_volume_sum_500  tree_sum_50  \\\n",
       "station_id                                                                  \n",
       "2651                   5.394371e+05             1.874661e+06        650.0   \n",
       "2652                   6.930643e+05             2.925047e+06        134.0   \n",
       "2653                   3.850106e+05             1.081819e+06        845.0   \n",
       "2655                   1.378667e+06             4.091401e+06        149.0   \n",
       "2656                   8.534503e+03             1.080769e+04       1950.0   \n",
       "\n",
       "            tree_sum_100  tree_sum_250  tree_sum_500  slope_mean_50  \\\n",
       "station_id                                                            \n",
       "2651              2281.0       12913.0       59364.0       6.258385   \n",
       "2652              1607.0       10679.0       38572.0       3.295830   \n",
       "2653              3211.0       18900.0       83168.0       6.053758   \n",
       "2655              1005.0        9609.0       33100.0       1.742169   \n",
       "2656              7070.0       40955.0      172565.0      27.618801   \n",
       "\n",
       "            slope_mean_100  slope_mean_250  slope_mean_500  \\\n",
       "station_id                                                   \n",
       "2651              5.147757        3.865617        3.948312   \n",
       "2652              3.792125        3.354241        3.435203   \n",
       "2653              5.753644        6.210232        8.461916   \n",
       "2655              1.645347        2.092990        3.686358   \n",
       "2656             29.190599       28.890853       29.256348   \n",
       "\n",
       "            topographic_position_index_mean_50  \\\n",
       "station_id                                       \n",
       "2651                                  0.002324   \n",
       "2652                                 -0.000874   \n",
       "2653                                  0.000385   \n",
       "2655                                 -0.000051   \n",
       "2656                                  0.011389   \n",
       "\n",
       "            topographic_position_index_mean_100  \\\n",
       "station_id                                        \n",
       "2651                                   0.001576   \n",
       "2652                                   0.000531   \n",
       "2653                                   0.000706   \n",
       "2655                                  -0.000204   \n",
       "2656                                   0.003584   \n",
       "\n",
       "            topographic_position_index_mean_250  \\\n",
       "station_id                                        \n",
       "2651                                   0.000070   \n",
       "2652                                  -0.000046   \n",
       "2653                                   0.000689   \n",
       "2655                                  -0.000106   \n",
       "2656                                   0.002101   \n",
       "\n",
       "            topographic_position_index_mean_500  \n",
       "station_id                                       \n",
       "2651                                  -0.000084  \n",
       "2652                                  -0.000022  \n",
       "2653                                   0.000174  \n",
       "2655                                  -0.000154  \n",
       "2656                                   0.000425  "
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fa = focalpy.FocalAnalysis(\n",
    "    [buildings_gdf, tree_canopy_filepath, dem_filepath],\n",
    "    stations_gdf,\n",
    "    buffer_dists,\n",
    "    [\n",
    "        \"compute_vector_features\",\n",
    "        \"compute_raster_features\",\n",
    "        \"compute_terrain_attributes\",\n",
    "    ],\n",
    "    feature_col_prefixes=[\"building\", \"tree\", \"\"],\n",
    "    feature_methods_args={\n",
    "        \"compute_vector_features\": [{\"volume\": \"sum\"}],\n",
    "        \"compute_terrain_attributes\": [[\"slope\", \"topographic_position_index\"]],\n",
    "    },\n",
    "    feature_methods_kwargs={\n",
    "        \"compute_raster_features\": {\"stats\": \"sum\"},\n",
    "        \"compute_terrain_attributes\": {\"stats\": \"mean\"},\n",
    "    },\n",
    ")\n",
    "fa.features_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9",
   "metadata": {},
   "source": [
    "### Vector data\n",
    "\n",
    "#### Basic aggregation with GeoPandas\n",
    "\n",
    "Vector datasets such as building footprints or road networks provide essential descriptors of the built environment and land use. Spatial predictors can be obtained by spatially joining each focal buffer with the vector layer and computing summary statistics (e.g., count, sum, mean...) of the polygon attributes within each buffer, for instance the total built-up area or the mean building height. To that end, we can use the `focalpy.compute_vector_features` functions, which wraps standard GeoPandas groupby/aggregate operations. For instance, we can compute the number of buildings by getting the `\"size\"` of each group:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "10",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
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       "      <th>size</th>\n",
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       "      <th>station_id</th>\n",
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       "      <th rowspan=\"4\" valign=\"top\">2651</th>\n",
       "      <th>50</th>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>19.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>250</th>\n",
       "      <td>124.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>500</th>\n",
       "      <td>552.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2652</th>\n",
       "      <th>50</th>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SMA</th>\n",
       "      <th>500</th>\n",
       "      <td>555.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">UEB</th>\n",
       "      <th>50</th>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>250</th>\n",
       "      <td>20.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>500</th>\n",
       "      <td>32.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>112 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                         size\n",
       "station_id buffer_dist       \n",
       "2651       50             6.0\n",
       "           100           19.0\n",
       "           250          124.0\n",
       "           500          552.0\n",
       "2652       50             8.0\n",
       "...                       ...\n",
       "SMA        500          555.0\n",
       "UEB        50             2.0\n",
       "           100            2.0\n",
       "           250           20.0\n",
       "           500           32.0\n",
       "\n",
       "[112 rows x 1 columns]"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "focalpy.compute_vector_features(\n",
    "    buildings_gdf, stations_gdf, buffer_dists, gb_reduce_method=\"size\"\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "11",
   "metadata": {},
   "source": [
    "We can also compute the per-buffer mean of each column:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "12",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>height_mean</th>\n",
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       "      <th>station_id</th>\n",
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       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">2651</th>\n",
       "      <th>50</th>\n",
       "      <td>16.863535</td>\n",
       "      <td>423.278173</td>\n",
       "      <td>7133.208817</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>15.645063</td>\n",
       "      <td>427.571637</td>\n",
       "      <td>6846.705932</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>250</th>\n",
       "      <td>10.056238</td>\n",
       "      <td>339.999499</td>\n",
       "      <td>4350.298985</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>500</th>\n",
       "      <td>8.837930</td>\n",
       "      <td>278.871534</td>\n",
       "      <td>3396.125261</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2652</th>\n",
       "      <th>50</th>\n",
       "      <td>18.997770</td>\n",
       "      <td>191.284786</td>\n",
       "      <td>3611.376583</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>SMA</th>\n",
       "      <th>500</th>\n",
       "      <td>8.645853</td>\n",
       "      <td>192.181419</td>\n",
       "      <td>1837.771486</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">UEB</th>\n",
       "      <th>50</th>\n",
       "      <td>2.683603</td>\n",
       "      <td>183.051163</td>\n",
       "      <td>973.190031</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>2.683603</td>\n",
       "      <td>183.051163</td>\n",
       "      <td>973.190031</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>250</th>\n",
       "      <td>9.355363</td>\n",
       "      <td>167.553049</td>\n",
       "      <td>1246.004972</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>500</th>\n",
       "      <td>9.658524</td>\n",
       "      <td>132.610983</td>\n",
       "      <td>984.392390</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>112 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                        height_mean   area_mean  volume_mean\n",
       "station_id buffer_dist                                      \n",
       "2651       50             16.863535  423.278173  7133.208817\n",
       "           100            15.645063  427.571637  6846.705932\n",
       "           250            10.056238  339.999499  4350.298985\n",
       "           500             8.837930  278.871534  3396.125261\n",
       "2652       50             18.997770  191.284786  3611.376583\n",
       "...                             ...         ...          ...\n",
       "SMA        500             8.645853  192.181419  1837.771486\n",
       "UEB        50              2.683603  183.051163   973.190031\n",
       "           100             2.683603  183.051163   973.190031\n",
       "           250             9.355363  167.553049  1246.004972\n",
       "           500             9.658524  132.610983   984.392390\n",
       "\n",
       "[112 rows x 3 columns]"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "focalpy.compute_vector_features(buildings_gdf, stations_gdf, buffer_dists, \"mean\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "13",
   "metadata": {},
   "source": [
    "aggregate each column differently:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "14",
   "metadata": {},
   "outputs": [
    {
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       "                             area_sum  height_mean\n",
       "station_id buffer_dist                            \n",
       "2651       50             2539.669037    16.863535\n",
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       "...                               ...          ...\n",
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       "           100             366.102327     2.683603\n",
       "           250            3351.060986     9.355363\n",
       "           500            4243.551471     9.658524\n",
       "\n",
       "[112 rows x 2 columns]"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "focalpy.compute_vector_features(\n",
    "    buildings_gdf, stations_gdf, buffer_dists, {\"area\": \"sum\", \"height\": \"mean\"}\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "15",
   "metadata": {},
   "source": [
    "or compute several aggregations for all columns:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "16",
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [
    {
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       "      <td>1.019963e+06</td>\n",
       "      <td>1837.771486</td>\n",
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       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">UEB</th>\n",
       "      <th>50</th>\n",
       "      <td>5.367206</td>\n",
       "      <td>2.683603</td>\n",
       "      <td>366.102327</td>\n",
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       "      <td>1.946380e+03</td>\n",
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       "      <td>132.610983</td>\n",
       "      <td>3.150056e+04</td>\n",
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       "                         height_sum  height_mean       area_sum   area_mean  \\\n",
       "station_id buffer_dist                                                        \n",
       "2651       50            101.181213    16.863535    2539.669037  423.278173   \n",
       "           100           297.256202    15.645063    8123.861097  427.571637   \n",
       "           250          1246.973527    10.056238   42159.937823  339.999499   \n",
       "           500          4878.537480     8.837930  153937.086847  278.871534   \n",
       "2652       50            151.982158    18.997770    1530.278291  191.284786   \n",
       "...                             ...          ...            ...         ...   \n",
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       "           500           309.072774     9.658524    4243.551471  132.610983   \n",
       "\n",
       "                          volume_sum  volume_mean  \n",
       "station_id buffer_dist                             \n",
       "2651       50           4.279925e+04  7133.208817  \n",
       "           100          1.300874e+05  6846.705932  \n",
       "           250          5.394371e+05  4350.298985  \n",
       "           500          1.874661e+06  3396.125261  \n",
       "2652       50           2.889101e+04  3611.376583  \n",
       "...                              ...          ...  \n",
       "SMA        500          1.019963e+06  1837.771486  \n",
       "UEB        50           1.946380e+03   973.190031  \n",
       "           100          1.946380e+03   973.190031  \n",
       "           250          2.492010e+04  1246.004972  \n",
       "           500          3.150056e+04   984.392390  \n",
       "\n",
       "[112 rows x 6 columns]"
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     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "focalpy.compute_vector_features(\n",
    "    buildings_gdf, stations_gdf, buffer_dists, [\"sum\", \"mean\"]\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "17",
   "metadata": {},
   "source": [
    "we can further estimate the building volume as a product of the area and height and compute its mean:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "18",
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "                        volume_mean\n",
       "station_id buffer_dist             \n",
       "2651       50           7133.208817\n",
       "           100          6846.705932\n",
       "           250          4350.298985\n",
       "           500          3396.125261\n",
       "2652       50           3611.376583\n",
       "...                             ...\n",
       "SMA        500          1837.771486\n",
       "UEB        50            973.190031\n",
       "           100           973.190031\n",
       "           250          1246.004972\n",
       "           500           984.392390\n",
       "\n",
       "[112 rows x 1 columns]"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "focalpy.compute_vector_features(\n",
    "    buildings_gdf.assign(**{\"volume\": buildings_gdf[\"area\"] * buildings_gdf[\"height\"]}),\n",
    "    stations_gdf,\n",
    "    buffer_dists,\n",
    "    {\"volume\": \"mean\"},\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "19",
   "metadata": {
    "lines_to_next_cell": 2
   },
   "source": [
    "#### Urban morphometrics with momepy\n",
    "\n",
    "Beyond simple aggregations, [momepy](https://momepy.org) <cite data-cite=\"fleischmann2019momepy\"/> provides a comprehensive toolkit of urban morphometric indicators that characterise the shape, size, and spatial arrangement of urban elements. These include measures of compactness (e.g. circular compactness), elongation, fractal dimension and many others that capture the three-dimensional complexity of the built environment. `focalpy` wraps `momepy` metrics via `compute_urban_morphometrics`, computing and aggregating them within each focal buffer. We only need to provide the list of target momepy metrics and aggregation function:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "20",
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>circular_compactness_mean</th>\n",
       "      <th>fractal_dimension_mean</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>station_id</th>\n",
       "      <th>buffer_dist</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">2651</th>\n",
       "      <th>50</th>\n",
       "      <td>0.553636</td>\n",
       "      <td>1.022197</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>0.550181</td>\n",
       "      <td>1.023150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>250</th>\n",
       "      <td>0.538925</td>\n",
       "      <td>1.030278</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>500</th>\n",
       "      <td>0.519466</td>\n",
       "      <td>1.041202</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2652</th>\n",
       "      <th>50</th>\n",
       "      <td>0.538450</td>\n",
       "      <td>1.022200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SMA</th>\n",
       "      <th>500</th>\n",
       "      <td>0.550440</td>\n",
       "      <td>1.034010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">UEB</th>\n",
       "      <th>50</th>\n",
       "      <td>0.700509</td>\n",
       "      <td>0.916253</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>0.700509</td>\n",
       "      <td>0.916253</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>250</th>\n",
       "      <td>0.517528</td>\n",
       "      <td>1.049154</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>500</th>\n",
       "      <td>0.530266</td>\n",
       "      <td>1.046731</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>112 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                        circular_compactness_mean  fractal_dimension_mean\n",
       "station_id buffer_dist                                                   \n",
       "2651       50                            0.553636                1.022197\n",
       "           100                           0.550181                1.023150\n",
       "           250                           0.538925                1.030278\n",
       "           500                           0.519466                1.041202\n",
       "2652       50                            0.538450                1.022200\n",
       "...                                           ...                     ...\n",
       "SMA        500                           0.550440                1.034010\n",
       "UEB        50                            0.700509                0.916253\n",
       "           100                           0.700509                0.916253\n",
       "           250                           0.517528                1.049154\n",
       "           500                           0.530266                1.046731\n",
       "\n",
       "[112 rows x 2 columns]"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "focalpy.compute_urban_morphometrics(\n",
    "    buildings_gdf,\n",
    "    stations_gdf,\n",
    "    buffer_dists,\n",
    "    [\"circular_compactness\", \"fractal_dimension\"],\n",
    "    \"mean\",\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "21",
   "metadata": {},
   "source": [
    "it is also possible to pass keyword arguments to each momepy function:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "22",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>circular_compactness_mean</th>\n",
       "      <th>form_factor_mean</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>station_id</th>\n",
       "      <th>buffer_dist</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">2651</th>\n",
       "      <th>50</th>\n",
       "      <td>0.553636</td>\n",
       "      <td>5.146849</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>0.550181</td>\n",
       "      <td>5.183737</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>250</th>\n",
       "      <td>0.538925</td>\n",
       "      <td>36.460616</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>500</th>\n",
       "      <td>0.519466</td>\n",
       "      <td>15.382184</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2652</th>\n",
       "      <th>50</th>\n",
       "      <td>0.538450</td>\n",
       "      <td>5.587796</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SMA</th>\n",
       "      <th>500</th>\n",
       "      <td>0.550440</td>\n",
       "      <td>5.911815</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">UEB</th>\n",
       "      <th>50</th>\n",
       "      <td>0.700509</td>\n",
       "      <td>19.259741</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>0.700509</td>\n",
       "      <td>19.259741</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>250</th>\n",
       "      <td>0.517528</td>\n",
       "      <td>24.733243</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>500</th>\n",
       "      <td>0.530266</td>\n",
       "      <td>17.679633</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>112 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                        circular_compactness_mean  form_factor_mean\n",
       "station_id buffer_dist                                             \n",
       "2651       50                            0.553636          5.146849\n",
       "           100                           0.550181          5.183737\n",
       "           250                           0.538925         36.460616\n",
       "           500                           0.519466         15.382184\n",
       "2652       50                            0.538450          5.587796\n",
       "...                                           ...               ...\n",
       "SMA        500                           0.550440          5.911815\n",
       "UEB        50                            0.700509         19.259741\n",
       "           100                           0.700509         19.259741\n",
       "           250                           0.517528         24.733243\n",
       "           500                           0.530266         17.679633\n",
       "\n",
       "[112 rows x 2 columns]"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "focalpy.compute_urban_morphometrics(\n",
    "    buildings_gdf,\n",
    "    stations_gdf,\n",
    "    buffer_dists,\n",
    "    [\"circular_compactness\", \"form_factor\"],\n",
    "    \"mean\",\n",
    "    momepy_metrics_kwargs_dict={\"form_factor\": {\"height\": buildings_gdf[\"height\"]}},\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "23",
   "metadata": {},
   "source": [
    "### Raster data\n",
    "\n",
    "#### Zonal statistics with rasterstats\n",
    "\n",
    "Raster layers - such as digital elevation models (DEM), land use/land cover (LULC) maps, or remote-sensing-derived indices - can be summarised within each focal buffer through zonal statistics, i.e. computing aggregate functions (mean, sum, min, max...) over the pixel values that fall within the buffer. To that end, `focalpy` uses [rasterstats](https://github.com/perrygeo/python-rasterstats) under the hood for this purpose via the `compute_raster_features` function. For instance, we can compute the mean altitude within each buffer by providing a raster DEM:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "24",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>mean</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>station_id</th>\n",
       "      <th>buffer_dist</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">2651</th>\n",
       "      <th>50</th>\n",
       "      <td>455.053617</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>454.768099</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>250</th>\n",
       "      <td>453.078239</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>500</th>\n",
       "      <td>453.072617</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2652</th>\n",
       "      <th>50</th>\n",
       "      <td>406.412919</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SMA</th>\n",
       "      <th>500</th>\n",
       "      <td>560.063654</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">UEB</th>\n",
       "      <th>50</th>\n",
       "      <td>853.093014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>844.082015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>250</th>\n",
       "      <td>803.673862</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>500</th>\n",
       "      <td>748.301653</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>112 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                              mean\n",
       "station_id buffer_dist            \n",
       "2651       50           455.053617\n",
       "           100          454.768099\n",
       "           250          453.078239\n",
       "           500          453.072617\n",
       "2652       50           406.412919\n",
       "...                            ...\n",
       "SMA        500          560.063654\n",
       "UEB        50           853.093014\n",
       "           100          844.082015\n",
       "           250          803.673862\n",
       "           500          748.301653\n",
       "\n",
       "[112 rows x 1 columns]"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "focalpy.compute_raster_features(\n",
    "    dem_arr,\n",
    "    stations_gdf,\n",
    "    buffer_dists,\n",
    "    affine=dem_transform,\n",
    "    stats=\"mean\",\n",
    "    nodata=dem_nodata,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "25",
   "metadata": {},
   "source": [
    "We can also use it to compute the sum and mean of pixels of a given LULC class, e.g., urban green spaces:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "26",
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/martibosch/libraries/focalpy/.pixi/envs/user-guide/lib/python3.13/site-packages/rasterstats/io.py:335: NodataWarning: Setting nodata to -999; specify nodata explicitly\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>mean</th>\n",
       "      <th>sum</th>\n",
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       "    <tr>\n",
       "      <th>station_id</th>\n",
       "      <th>buffer_dist</th>\n",
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       "      <th rowspan=\"4\" valign=\"top\">2651</th>\n",
       "      <th>50</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>250</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>500</th>\n",
       "      <td>0.000801</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2652</th>\n",
       "      <th>50</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SMA</th>\n",
       "      <th>500</th>\n",
       "      <td>0.004785</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">UEB</th>\n",
       "      <th>50</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>250</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>500</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>112 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                            mean  sum\n",
       "station_id buffer_dist               \n",
       "2651       50           0.000000  0.0\n",
       "           100          0.000000  0.0\n",
       "           250          0.000000  0.0\n",
       "           500          0.000801  1.0\n",
       "2652       50           0.000000  0.0\n",
       "...                          ...  ...\n",
       "SMA        500          0.004785  6.0\n",
       "UEB        50           0.000000  0.0\n",
       "           100          0.000000  0.0\n",
       "           250          0.000000  0.0\n",
       "           500          0.000000  0.0\n",
       "\n",
       "[112 rows x 2 columns]"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "with rio.open(lulc_filepath) as src:\n",
    "    # read the one and only band\n",
    "    lulc_arr = src.read(1)\n",
    "    lulc_transform = src.transform\n",
    "\n",
    "target_class = 5  # urban green spaces\n",
    "focalpy.compute_raster_features(\n",
    "    lulc_arr == target_class,\n",
    "    stations_gdf,\n",
    "    buffer_dists,\n",
    "    affine=lulc_transform,\n",
    "    stats=[\"sum\", \"mean\"],\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "27",
   "metadata": {},
   "source": [
    "### Landscape metrics with PyLandStats\n",
    "\n",
    "Landscape ecology provides numerous metrics to quantify the spatial composition and configuration of habitat patches, including proportion of landscape, edge density, patch shape complexity, and diversity indices <cite data-cite=\"riitters2019pattern\"/>. In `focalpy`, the `compute_landscape_metrics` function enables easy computation of landscape metrics for each buffer using [PyLandStats](https://pylandstats.readthedocs.io) <cite data-cite=\"bosch2019pylandstats\"/>. For instance, we can provide a list of class-level and landscape-level metrics as well as a list of classes to include:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "28",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[########################################] | 100% Completed | 102.04 ms\n",
      "[########################################] | 100% Completed | 100.99 ms\n",
      "[                                        ] | 0% Completed | 150.59 us"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/martibosch/libraries/focalpy/.pixi/envs/user-guide/lib/python3.13/site-packages/pylandstats/landscape.py:3533: RuntimeWarning: Entropy-based metrics can only be computed in landscapes with at least two classes of patches. Returning nan\n",
      "  warnings.warn(\n",
      "/home/martibosch/libraries/focalpy/.pixi/envs/user-guide/lib/python3.13/site-packages/pylandstats/landscape.py:3533: RuntimeWarning: Entropy-based metrics can only be computed in landscapes with at least two classes of patches. Returning nan\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[########################################] | 100% Completed | 101.35 ms\n",
      "[########################################] | 100% Completed | 303.72 ms\n",
      "[########################################] | 100% Completed | 206.85 ms\n",
      "[########################################] | 100% Completed | 100.87 ms\n",
      "[########################################] | 100% Completed | 212.66 ms\n",
      "[########################################] | 100% Completed | 101.74 ms\n"
     ]
    },
    {
     "data": {
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       "      <th></th>\n",
       "      <th>proportion_of_landscape_5</th>\n",
       "      <th>proportion_of_landscape_8</th>\n",
       "      <th>proportion_of_landscape_10</th>\n",
       "      <th>edge_density_5</th>\n",
       "      <th>edge_density_8</th>\n",
       "      <th>edge_density_10</th>\n",
       "      <th>shannon_diversity_index</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>station_id</th>\n",
       "      <th>buffer_dist</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 rowspan=\"4\" valign=\"top\">2651</th>\n",
       "      <th>50</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.691416</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.655178</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>250</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.935484</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>16.774194</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.833076</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>500</th>\n",
       "      <td>0.080064</td>\n",
       "      <td>5.364291</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.281025</td>\n",
       "      <td>31.385108</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.933643</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2652</th>\n",
       "      <th>50</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.529706</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SMA</th>\n",
       "      <th>500</th>\n",
       "      <td>0.478469</td>\n",
       "      <td>0.956938</td>\n",
       "      <td>8.213716</td>\n",
       "      <td>2.551834</td>\n",
       "      <td>8.293461</td>\n",
       "      <td>24.880383</td>\n",
       "      <td>1.193421</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">UEB</th>\n",
       "      <th>50</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>76.923077</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>184.615385</td>\n",
       "      <td>0.676720</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>78.431373</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>156.862745</td>\n",
       "      <td>0.735071</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>250</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>66.773163</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>173.801917</td>\n",
       "      <td>1.054298</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>500</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.556881</td>\n",
       "      <td>75.417661</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.500398</td>\n",
       "      <td>144.789181</td>\n",
       "      <td>0.827920</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>112 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                        proportion_of_landscape_5  proportion_of_landscape_8  \\\n",
       "station_id buffer_dist                                                         \n",
       "2651       50                            0.000000                   0.000000   \n",
       "           100                           0.000000                   0.000000   \n",
       "           250                           0.000000                   1.935484   \n",
       "           500                           0.080064                   5.364291   \n",
       "2652       50                            0.000000                   0.000000   \n",
       "...                                           ...                        ...   \n",
       "SMA        500                           0.478469                   0.956938   \n",
       "UEB        50                            0.000000                   0.000000   \n",
       "           100                           0.000000                   0.000000   \n",
       "           250                           0.000000                   0.000000   \n",
       "           500                           0.000000                   0.556881   \n",
       "\n",
       "                        proportion_of_landscape_10  edge_density_5  \\\n",
       "station_id buffer_dist                                               \n",
       "2651       50                             0.000000        0.000000   \n",
       "           100                            0.000000        0.000000   \n",
       "           250                            0.000000        0.000000   \n",
       "           500                            0.000000        1.281025   \n",
       "2652       50                             0.000000        0.000000   \n",
       "...                                            ...             ...   \n",
       "SMA        500                            8.213716        2.551834   \n",
       "UEB        50                            76.923077        0.000000   \n",
       "           100                           78.431373        0.000000   \n",
       "           250                           66.773163        0.000000   \n",
       "           500                           75.417661        0.000000   \n",
       "\n",
       "                        edge_density_8  edge_density_10  \\\n",
       "station_id buffer_dist                                    \n",
       "2651       50                 0.000000         0.000000   \n",
       "           100                0.000000         0.000000   \n",
       "           250               16.774194         0.000000   \n",
       "           500               31.385108         0.000000   \n",
       "2652       50                 0.000000         0.000000   \n",
       "...                                ...              ...   \n",
       "SMA        500                8.293461        24.880383   \n",
       "UEB        50                 0.000000       184.615385   \n",
       "           100                0.000000       156.862745   \n",
       "           250                0.000000       173.801917   \n",
       "           500                3.500398       144.789181   \n",
       "\n",
       "                        shannon_diversity_index  \n",
       "station_id buffer_dist                           \n",
       "2651       50                          0.691416  \n",
       "           100                         0.655178  \n",
       "           250                         0.833076  \n",
       "           500                         0.933643  \n",
       "2652       50                          0.529706  \n",
       "...                                         ...  \n",
       "SMA        500                         1.193421  \n",
       "UEB        50                          0.676720  \n",
       "           100                         0.735071  \n",
       "           250                         1.054298  \n",
       "           500                         0.827920  \n",
       "\n",
       "[112 rows x 7 columns]"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# green spaces (5), meadows/pastures (8) and forests (10)\n",
    "focalpy.compute_landscape_metrics(\n",
    "    lulc_filepath,\n",
    "    stations_gdf,\n",
    "    buffer_dists,\n",
    "    class_metrics=[\"proportion_of_landscape\", \"edge_density\"],\n",
    "    landscape_metrics=[\"shannon_diversity_index\"],\n",
    "    classes=[5, 8, 10],\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "29",
   "metadata": {},
   "source": [
    "### Terrain attributes with xDEM\n",
    "\n",
    "Topography exerts a strong influence on many environmental processes, from cold-air drainage in urban valleys to hydrological connectivity and solar radiation exposure. Terrain attributes such as slope, aspect, curvature, and the topographic position index (TPI) can be derived from a digital elevation model (DEM). To that end, `focalpy` uses [xDEM](https://xdem.readthedocs.io) <cite data-cite=\"hugonnet2022uncertainty\"/> to compute these attributes and then aggregates them within each focal buffer via the `compute_terrain_attributes` function. For example, we can compute the mean and max slope for each site and buffer:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "30",
   "metadata": {},
   "outputs": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>slope_mean</th>\n",
       "      <th>slope_max</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>station_id</th>\n",
       "      <th>buffer_dist</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">2651</th>\n",
       "      <th>50</th>\n",
       "      <td>6.258385</td>\n",
       "      <td>46.536942</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>5.147757</td>\n",
       "      <td>46.536942</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>250</th>\n",
       "      <td>3.865617</td>\n",
       "      <td>50.672459</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>500</th>\n",
       "      <td>3.948312</td>\n",
       "      <td>50.672459</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2652</th>\n",
       "      <th>50</th>\n",
       "      <td>3.295830</td>\n",
       "      <td>26.684357</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SMA</th>\n",
       "      <th>500</th>\n",
       "      <td>10.515907</td>\n",
       "      <td>61.013931</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">UEB</th>\n",
       "      <th>50</th>\n",
       "      <td>7.556008</td>\n",
       "      <td>47.828327</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>20.953310</td>\n",
       "      <td>71.454239</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>250</th>\n",
       "      <td>30.595184</td>\n",
       "      <td>79.063553</td>\n",
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       "    <tr>\n",
       "      <th>500</th>\n",
       "      <td>29.370031</td>\n",
       "      <td>79.063553</td>\n",
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       "  </tbody>\n",
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       "<p>112 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                        slope_mean  slope_max\n",
       "station_id buffer_dist                       \n",
       "2651       50             6.258385  46.536942\n",
       "           100            5.147757  46.536942\n",
       "           250            3.865617  50.672459\n",
       "           500            3.948312  50.672459\n",
       "2652       50             3.295830  26.684357\n",
       "...                            ...        ...\n",
       "SMA        500           10.515907  61.013931\n",
       "UEB        50             7.556008  47.828327\n",
       "           100           20.953310  71.454239\n",
       "           250           30.595184  79.063553\n",
       "           500           29.370031  79.063553\n",
       "\n",
       "[112 rows x 2 columns]"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "focalpy.compute_terrain_attributes(\n",
    "    dem_filepath, stations_gdf, buffer_dists, \"slope\", stats=[\"mean\", \"max\"]\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "31",
   "metadata": {},
   "source": [
    "or compute several metrics, e.g., adding TPI:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "32",
   "metadata": {},
   "outputs": [
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       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>slope_mean</th>\n",
       "      <th>topographic_position_index_mean</th>\n",
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       "    <tr>\n",
       "      <th>station_id</th>\n",
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       "      <td>6.258385</td>\n",
       "      <td>0.002324</td>\n",
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       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>5.147757</td>\n",
       "      <td>0.001576</td>\n",
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       "    <tr>\n",
       "      <th>250</th>\n",
       "      <td>3.865617</td>\n",
       "      <td>0.000070</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>500</th>\n",
       "      <td>3.948312</td>\n",
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       "    <tr>\n",
       "      <th>2652</th>\n",
       "      <th>50</th>\n",
       "      <td>3.295830</td>\n",
       "      <td>-0.000874</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SMA</th>\n",
       "      <th>500</th>\n",
       "      <td>10.515907</td>\n",
       "      <td>-0.000143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">UEB</th>\n",
       "      <th>50</th>\n",
       "      <td>7.556008</td>\n",
       "      <td>0.015926</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>20.953310</td>\n",
       "      <td>0.015927</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>250</th>\n",
       "      <td>30.595184</td>\n",
       "      <td>0.004503</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>500</th>\n",
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       "      <td>0.001672</td>\n",
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      "text/plain": [
       "                        slope_mean  topographic_position_index_mean\n",
       "station_id buffer_dist                                             \n",
       "2651       50             6.258385                         0.002324\n",
       "           100            5.147757                         0.001576\n",
       "           250            3.865617                         0.000070\n",
       "           500            3.948312                        -0.000084\n",
       "2652       50             3.295830                        -0.000874\n",
       "...                            ...                              ...\n",
       "SMA        500           10.515907                        -0.000143\n",
       "UEB        50             7.556008                         0.015926\n",
       "           100           20.953310                         0.015927\n",
       "           250           30.595184                         0.004503\n",
       "           500           29.370031                         0.001672\n",
       "\n",
       "[112 rows x 2 columns]"
      ]
     },
     "execution_count": null,
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   ],
   "source": [
    "focalpy.compute_terrain_attributes(\n",
    "    dem_filepath,\n",
    "    stations_gdf,\n",
    "    buffer_dists,\n",
    "    [\"slope\", \"topographic_position_index\"],\n",
    "    stats=\"mean\",\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "33",
   "metadata": {},
   "source": [
    "Additionally, we can aggregate each attribute differently by providing a list of `stats` that positionally match the list of metrics:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "34",
   "metadata": {},
   "outputs": [
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       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>slope_mean</th>\n",
       "      <th>slope_max</th>\n",
       "      <th>topographic_position_index_mean</th>\n",
       "      <th>topographic_position_index_max</th>\n",
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       "    <tr>\n",
       "      <th>station_id</th>\n",
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       "      <td>0.002324</td>\n",
       "      <td>1.613647</td>\n",
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       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>5.147757</td>\n",
       "      <td>46.536942</td>\n",
       "      <td>0.001576</td>\n",
       "      <td>1.613647</td>\n",
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       "    <tr>\n",
       "      <th>250</th>\n",
       "      <td>3.865617</td>\n",
       "      <td>50.672459</td>\n",
       "      <td>0.000070</td>\n",
       "      <td>1.746887</td>\n",
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       "    <tr>\n",
       "      <th>500</th>\n",
       "      <td>3.948312</td>\n",
       "      <td>50.672459</td>\n",
       "      <td>-0.000084</td>\n",
       "      <td>1.901245</td>\n",
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       "    <tr>\n",
       "      <th>2652</th>\n",
       "      <th>50</th>\n",
       "      <td>3.295830</td>\n",
       "      <td>26.684357</td>\n",
       "      <td>-0.000874</td>\n",
       "      <td>0.532562</td>\n",
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       "      <td>...</td>\n",
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       "      <th>SMA</th>\n",
       "      <th>500</th>\n",
       "      <td>10.515907</td>\n",
       "      <td>61.013931</td>\n",
       "      <td>-0.000143</td>\n",
       "      <td>3.343628</td>\n",
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       "      <th rowspan=\"4\" valign=\"top\">UEB</th>\n",
       "      <th>50</th>\n",
       "      <td>7.556008</td>\n",
       "      <td>47.828327</td>\n",
       "      <td>0.015926</td>\n",
       "      <td>0.703735</td>\n",
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       "      <td>0.015927</td>\n",
       "      <td>2.174011</td>\n",
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       "    <tr>\n",
       "      <th>250</th>\n",
       "      <td>30.595184</td>\n",
       "      <td>79.063553</td>\n",
       "      <td>0.004503</td>\n",
       "      <td>6.606323</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>500</th>\n",
       "      <td>29.370031</td>\n",
       "      <td>79.063553</td>\n",
       "      <td>0.001672</td>\n",
       "      <td>6.606323</td>\n",
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       "<p>112 rows × 4 columns</p>\n",
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      "text/plain": [
       "                        slope_mean  slope_max  \\\n",
       "station_id buffer_dist                          \n",
       "2651       50             6.258385  46.536942   \n",
       "           100            5.147757  46.536942   \n",
       "           250            3.865617  50.672459   \n",
       "           500            3.948312  50.672459   \n",
       "2652       50             3.295830  26.684357   \n",
       "...                            ...        ...   \n",
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       "           250           30.595184  79.063553   \n",
       "           500           29.370031  79.063553   \n",
       "\n",
       "                        topographic_position_index_mean  \\\n",
       "station_id buffer_dist                                    \n",
       "2651       50                                  0.002324   \n",
       "           100                                 0.001576   \n",
       "           250                                 0.000070   \n",
       "           500                                -0.000084   \n",
       "2652       50                                 -0.000874   \n",
       "...                                                 ...   \n",
       "SMA        500                                -0.000143   \n",
       "UEB        50                                  0.015926   \n",
       "           100                                 0.015927   \n",
       "           250                                 0.004503   \n",
       "           500                                 0.001672   \n",
       "\n",
       "                        topographic_position_index_max  \n",
       "station_id buffer_dist                                  \n",
       "2651       50                                 1.613647  \n",
       "           100                                1.613647  \n",
       "           250                                1.746887  \n",
       "           500                                1.901245  \n",
       "2652       50                                 0.532562  \n",
       "...                                                ...  \n",
       "SMA        500                                3.343628  \n",
       "UEB        50                                 0.703735  \n",
       "           100                                2.174011  \n",
       "           250                                6.606323  \n",
       "           500                                6.606323  \n",
       "\n",
       "[112 rows x 4 columns]"
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     "metadata": {},
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   ],
   "source": [
    "focalpy.compute_terrain_attributes(\n",
    "    dem_filepath,\n",
    "    stations_gdf,\n",
    "    buffer_dists,\n",
    "    [\"slope\", \"topographic_position_index\"],\n",
    "    stats=[\"mean\", \"max\"],\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "35",
   "metadata": {},
   "source": [
    "### Embedding features\n",
    "\n",
    "Foundation models for Earth observation learn compact, semantically rich representations of the landscape directly from satellite time series, without requiring hand-crafted feature engineering.\n",
    "In `focalpy`, geoembeddings from foundational models can be used as multi-scale focal predictors via `compute_raster_features` with the `indexes` parameter, which treats each embedding dimension as a separate band and aggregates it within each focal buffer. For instance, we use the embeddings of the TESSERA <cite data-cite=\"feng2026tessera\"/> model (provided by the [TesseraCH](https://www.swissdatacube.org) instance within the Swiss Data Cube <cite data-cite=\"giuliani2017building\"/>), which produces per-pixel 16-dimensional embeddings from Sentinel-2 imagery at 10 m resolution in `EPSG:2056`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "36",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/martibosch/libraries/focalpy/.pixi/envs/user-guide/lib/python3.13/site-packages/rasterstats/io.py:335: NodataWarning: Setting nodata to -999; specify nodata explicitly\n",
      "  warnings.warn(\n"
     ]
    },
    {
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       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>7.801788</td>\n",
       "      <td>-0.283322</td>\n",
       "      <td>-17.009456</td>\n",
       "      <td>-3.153286</td>\n",
       "      <td>0.222913</td>\n",
       "      <td>0.181502</td>\n",
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       "      <td>8.385496</td>\n",
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       "      <th>250</th>\n",
       "      <td>9.202094</td>\n",
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       "      <td>-2.902726</td>\n",
       "      <td>-1.045133</td>\n",
       "      <td>1.306718</td>\n",
       "      <td>-6.896602</td>\n",
       "      <td>8.253628</td>\n",
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       "      <th>500</th>\n",
       "      <td>9.624077</td>\n",
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       "      <td>-17.975421</td>\n",
       "      <td>-3.103112</td>\n",
       "      <td>-0.890727</td>\n",
       "      <td>1.544997</td>\n",
       "      <td>-6.428439</td>\n",
       "      <td>7.644577</td>\n",
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       "      <th>2652</th>\n",
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       "      <td>-15.805838</td>\n",
       "      <td>-5.929575</td>\n",
       "      <td>-4.202278</td>\n",
       "      <td>-2.248786</td>\n",
       "      <td>-3.524276</td>\n",
       "      <td>12.881348</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>SMA</th>\n",
       "      <th>500</th>\n",
       "      <td>9.711702</td>\n",
       "      <td>4.309838</td>\n",
       "      <td>-17.336694</td>\n",
       "      <td>-2.147834</td>\n",
       "      <td>-0.544849</td>\n",
       "      <td>1.898409</td>\n",
       "      <td>-9.053722</td>\n",
       "      <td>7.642174</td>\n",
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       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">UEB</th>\n",
       "      <th>50</th>\n",
       "      <td>4.058430</td>\n",
       "      <td>7.008330</td>\n",
       "      <td>-16.849395</td>\n",
       "      <td>1.257009</td>\n",
       "      <td>-2.671455</td>\n",
       "      <td>4.563640</td>\n",
       "      <td>-4.611345</td>\n",
       "      <td>6.129853</td>\n",
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       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>3.942891</td>\n",
       "      <td>6.943526</td>\n",
       "      <td>-15.037635</td>\n",
       "      <td>2.932227</td>\n",
       "      <td>-3.973046</td>\n",
       "      <td>5.616287</td>\n",
       "      <td>-6.288865</td>\n",
       "      <td>4.988400</td>\n",
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       "    <tr>\n",
       "      <th>250</th>\n",
       "      <td>3.411806</td>\n",
       "      <td>3.192039</td>\n",
       "      <td>-15.017236</td>\n",
       "      <td>4.429847</td>\n",
       "      <td>-3.270214</td>\n",
       "      <td>5.941413</td>\n",
       "      <td>-7.680755</td>\n",
       "      <td>4.205943</td>\n",
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       "      <th>500</th>\n",
       "      <td>4.212605</td>\n",
       "      <td>5.104426</td>\n",
       "      <td>-14.209330</td>\n",
       "      <td>4.165118</td>\n",
       "      <td>-2.976318</td>\n",
       "      <td>5.433813</td>\n",
       "      <td>-7.133730</td>\n",
       "      <td>4.218562</td>\n",
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      "text/plain": [
       "                        tessera_1_mean  tessera_2_mean  tessera_3_mean  \\\n",
       "station_id buffer_dist                                                   \n",
       "2651       50                 7.892569        0.186058      -17.637870   \n",
       "           100                7.801788       -0.283322      -17.009456   \n",
       "           250                9.202094       -0.964912      -17.276590   \n",
       "           500                9.624077       -0.458643      -17.975421   \n",
       "2652       50                11.826660       -5.227532      -15.805838   \n",
       "...                                ...             ...             ...   \n",
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       "UEB        50                 4.058430        7.008330      -16.849395   \n",
       "           100                3.942891        6.943526      -15.037635   \n",
       "           250                3.411806        3.192039      -15.017236   \n",
       "           500                4.212605        5.104426      -14.209330   \n",
       "\n",
       "                        tessera_4_mean  tessera_5_mean  tessera_6_mean  \\\n",
       "station_id buffer_dist                                                   \n",
       "2651       50                -2.607904        0.613337        0.418400   \n",
       "           100               -3.153286        0.222913        0.181502   \n",
       "           250               -2.902726       -1.045133        1.306718   \n",
       "           500               -3.103112       -0.890727        1.544997   \n",
       "2652       50                -5.929575       -4.202278       -2.248786   \n",
       "...                                ...             ...             ...   \n",
       "SMA        500               -2.147834       -0.544849        1.898409   \n",
       "UEB        50                 1.257009       -2.671455        4.563640   \n",
       "           100                2.932227       -3.973046        5.616287   \n",
       "           250                4.429847       -3.270214        5.941413   \n",
       "           500                4.165118       -2.976318        5.433813   \n",
       "\n",
       "                        tessera_7_mean  tessera_8_mean  \n",
       "station_id buffer_dist                                  \n",
       "2651       50                -8.208691        8.698803  \n",
       "           100               -8.099371        8.385496  \n",
       "           250               -6.896602        8.253628  \n",
       "           500               -6.428439        7.644577  \n",
       "2652       50                -3.524276       12.881348  \n",
       "...                                ...             ...  \n",
       "SMA        500               -9.053722        7.642174  \n",
       "UEB        50                -4.611345        6.129853  \n",
       "           100               -6.288865        4.988400  \n",
       "           250               -7.680755        4.205943  \n",
       "           500               -7.133730        4.218562  \n",
       "\n",
       "[112 rows x 8 columns]"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "focalpy.compute_raster_features(\n",
    "    tessera_filepath,\n",
    "    stations_gdf,\n",
    "    buffer_dists,\n",
    "    indexes=range(1, 9),\n",
    "    band_prefix=\"tessera\",\n",
    "    stats=\"mean\",\n",
    ")"
   ]
  }
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