kissmygritts/resolve_overlapping_geoms.ipynb

## resolve_overlapping_geoms.ipynb
{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "153253f8-3f50-46d7-9824-0513c12a314b",
   "metadata": {},
   "outputs": [],
   "source": [
    "import geopandas as gpd\n",
    "import shapely\n",
    "from utils.gdf_helpers import resolve_overlapping_geoms"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "6cd1bc21-420a-478b-9c1b-4fb2024a75ea",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: >"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "geoms=[\n",
    "    shapely.geometry.Polygon.from_bounds(0, 0, 4, 4),\n",
    "    shapely.geometry.Polygon.from_bounds(2, 1, 6, 3),\n",
    "    shapely.geometry.Polygon.from_bounds(5, 5, 6, 6),\n",
    "    shapely.geometry.Polygon.from_bounds(0, 3, 1, 5.5),\n",
    "    shapely.geometry.Polygon.from_bounds(-1, 4.5, 2, 5.5),\n",
    "    shapely.geometry.Polygon.from_bounds(0, 5, 1, 6),\n",
    "]\n",
    "gdf = gpd.GeoDataFrame(\n",
    "    data={\"id\": range(len(geoms))}, \n",
    "    geometry=geoms\n",
    ")\n",
    "\n",
    "gdf.plot(color=[\"blue\", \"orange\", \"red\", \"green\", \"purple\", \"blue\"], alpha=.4, edgecolor=\"black\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "10fa600b-dee0-44be-9b48-966b9b968cb9",
   "metadata": {},
   "outputs": [],
   "source": [
    "resolved = resolve_overlapping_geoms(gdf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "f54178a8-ab3c-4717-94b0-b912744cf640",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: >"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "resolved.plot(color=[\"blue\", \"orange\", \"red\", \"green\", \"purple\", \"blue\"], alpha = .4, edgecolor=\"black\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "483a04ed-3315-4c0b-b74b-f7f8e3c07d57",
   "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>id</th>\n",
       "      <th>geometry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>POLYGON ((0.00000 0.00000, 0.00000 4.00000, 4....</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>POLYGON ((6.00000 3.00000, 6.00000 1.00000, 4....</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>POLYGON ((5.00000 5.00000, 5.00000 6.00000, 6....</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>POLYGON ((0.00000 4.00000, 0.00000 4.50000, 1....</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>POLYGON ((-1.00000 4.50000, -1.00000 5.50000, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>POLYGON ((0.00000 6.00000, 1.00000 6.00000, 1....</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   id                                           geometry\n",
       "0   0  POLYGON ((0.00000 0.00000, 0.00000 4.00000, 4....\n",
       "1   1  POLYGON ((6.00000 3.00000, 6.00000 1.00000, 4....\n",
       "2   2  POLYGON ((5.00000 5.00000, 5.00000 6.00000, 6....\n",
       "3   3  POLYGON ((0.00000 4.00000, 0.00000 4.50000, 1....\n",
       "4   4  POLYGON ((-1.00000 4.50000, -1.00000 5.50000, ...\n",
       "5   5  POLYGON ((0.00000 6.00000, 1.00000 6.00000, 1...."
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "resolved"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "a43fb252-124d-435f-b556-6683962637f1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['POLYGON ((0 0, 0 4, 4 4, 4 0, 0 0))',\n",
       " 'POLYGON ((6 3, 6 1, 4 1, 4 3, 6 3))',\n",
       " 'POLYGON ((5 5, 5 6, 6 6, 6 5, 5 5))',\n",
       " 'POLYGON ((0 4, 0 4.5, 1 4.5, 1 4, 0 4))',\n",
       " 'POLYGON ((-1 4.5, -1 5.5, 2 5.5, 2 4.5, -1 4.5))',\n",
       " 'POLYGON ((0 6, 1 6, 1 5.5, 0 5.5, 0 6))']"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[p.wkt for p in resolved.geometry]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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