{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "%matplotlib inline"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "\n# Polygonize raster\n\niMOD Python also provides convenience functions to polygonize rasters.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "import matplotlib.pyplot as plt"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "import imod"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "We'll start off by creating an example raster ``lake_grid`` to convert to\npolygons. This is similar to the `Rasterize shapefiles` example.\n\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "temp_dir = imod.util.temporary_directory()\nlakes = imod.data.lakes_shp(temp_dir)\n\n# Create dummy grid\nxmin = 90950.0\nxmax = 115650.0\ndx = 200\n\nymin = 445850.0\nymax = 467550.0\ndy = -200.0\n\nlike_2d = imod.util.empty_2d(dx, xmin, xmax, dy, ymin, ymax)\n\n# Rasterrize the shapes\nlake_grid = imod.prepare.rasterize(lakes, like=like_2d)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Our raster looks like this:\n\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "fig, ax = plt.subplots()\nlake_grid.plot(ax=ax)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Polygonize the lakes\n\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "polygonized_lakes = imod.prepare.polygonize(lake_grid)\n\npolygonized_lakes.head(5)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "This also polygonized the areas with np.nan. So we can drop those, using\nregular pandas functionality\n\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "polygonized_lakes = polygonized_lakes.dropna()\n\npolygonized_lakes.head(5)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Plotted, we see a similar picture to the plotted raster\n\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "fig, ax = plt.subplots()\npolygonized_lakes.plot(ax=ax)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Since it is a GeoDataFrame, we can now do vector operations. Like computing\nthe centroids and plotting them as points.\n\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "centroids = polygonized_lakes.centroid\n\nfig, ax = plt.subplots()\npolygonized_lakes.plot(ax=ax)\ncentroids.plot(ax=ax, color=\"k\")"
      ]
    }
  ],
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.10.5"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}