python/0.2.1.rioxarray.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "57855db3",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "os.chdir(\"../\")\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "import geopandas as gpd\n",
    "import rasterio\n",
    "import rioxarray\n",
    "from shapely.geometry import Point"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "3f9d45d4",
   "metadata": {},
   "outputs": [],
   "source": [
    "dc_dem = os.getcwd() + \"/data/raster/2018_dc_ndsf.tiff\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "69d1220c",
   "metadata": {},
   "outputs": [],
   "source": [
    "r = rioxarray.open_rasterio(dc_dem)\n",
    "r = r.squeeze().drop([\"spatial_ref\", \"band\"])\n",
    "r.name = \"elevation\"\n",
    "rdf = r.to_dataframe().reset_index()\n",
    "rdf[\"geometry\"] = [Point(x,y) for x, y in zip(rdf[\"x\"], rdf[\"y\"])]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "002cdaef",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([1.37937e+05, 2.05500e+04, 1.10700e+03, 3.51000e+02, 4.60000e+01,\n",
       "        4.00000e+00, 3.00000e+00, 0.00000e+00, 1.00000e+00, 1.00000e+00]),\n",
       " array([ -5.8047924 ,   5.53220081,  16.86919403,  28.2061882 ,\n",
       "         39.54318237,  50.88017273,  62.2171669 ,  73.55416107,\n",
       "         84.89115143,  96.22814941, 107.56513977]),\n",
       " <BarContainer object of 10 artists>)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "plt.hist(rdf.elevation)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "ox",
   "language": "python",
   "name": "ox"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.5"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {},
   "toc_section_display": true,
   "toc_window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}