Vizzuality/landgriffon

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data/notebooks/Lab/QA_raster_to_h3_data.ipynb

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{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "ac99e17e",
   "metadata": {},
   "source": [
    "# QA Raster to H3 conversion and test data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9db82340",
   "metadata": {},
   "source": [
    "This notebooks measures the amount of error we get from transforming raster data to H3 resolution 6 for cotton production, water risk and impact on a test area (India).  \n",
    "It also exports test data (as geojson and csv) for further testing against results from database queries. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c7436c79",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Install if needed\n",
    "#!pip install h3 --user\n",
    "#!pip install h3ronpy --user"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "1e85fd46",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Import libraries\n",
    "import h3\n",
    "from h3ronpy import raster\n",
    "import rasterio as rio\n",
    "import rasterio.plot\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from rasterstats import gen_zonal_stats, gen_point_query\n",
    "from rasterstats import zonal_stats\n",
    "import pandas as pd\n",
    "import geopandas as gpd\n",
    "import json\n",
    "import os\n",
    "from shapely.geometry import shape, mapping, box, Point, LinearRing, Polygon\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "51503180",
   "metadata": {},
   "outputs": [],
   "source": [
    "prod_raster = '../../datasets/processed/h3_test/cotton_production_ind.tif'\n",
    "\n",
    "test_area = (65,4,100,40)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "2545025b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Driver: GTiff/GeoTIFF\n",
      "Files: ../../datasets/processed/h3_test/cotton_production_ind.tif\n",
      "Size is 4320, 1668\n",
      "Coordinate System is:\n",
      "GEOGCRS[\"WGS 84\",\n",
      "    DATUM[\"World Geodetic System 1984\",\n",
      "        ELLIPSOID[\"WGS 84\",6378137,298.257223563,\n",
      "            LENGTHUNIT[\"metre\",1]]],\n",
      "    PRIMEM[\"Greenwich\",0,\n",
      "        ANGLEUNIT[\"degree\",0.0174532925199433]],\n",
      "    CS[ellipsoidal,2],\n",
      "        AXIS[\"geodetic latitude (Lat)\",north,\n",
      "            ORDER[1],\n",
      "            ANGLEUNIT[\"degree\",0.0174532925199433]],\n",
      "        AXIS[\"geodetic longitude (Lon)\",east,\n",
      "            ORDER[2],\n",
      "            ANGLEUNIT[\"degree\",0.0174532925199433]],\n",
      "    ID[\"EPSG\",4326]]\n",
      "Data axis to CRS axis mapping: 2,1\n",
      "Origin = (-179.991666650000013,83.097781811000004)\n",
      "Pixel Size = (0.083333340000000,-0.083333340000000)\n",
      "Metadata:\n",
      "  AREA_OR_POINT=Area\n",
      "Image Structure Metadata:\n",
      "  INTERLEAVE=BAND\n",
      "Corner Coordinates:\n",
      "Upper Left  (-179.9916667,  83.0977818) (179d59'30.00\"W, 83d 5'52.01\"N)\n",
      "Lower Left  (-179.9916667, -55.9022293) (179d59'30.00\"W, 55d54' 8.03\"S)\n",
      "Upper Right ( 180.0083622,  83.0977818) (180d 0'30.10\"E, 83d 5'52.01\"N)\n",
      "Lower Right ( 180.0083622, -55.9022293) (180d 0'30.10\"E, 55d54' 8.03\"S)\n",
      "Center      (   0.0083478,  13.5977763) (  0d 0'30.05\"E, 13d35'51.99\"N)\n",
      "Band 1 Block=4320x1 Type=Float32, ColorInterp=Gray\n",
      "  NoData Value=0\n"
     ]
    }
   ],
   "source": [
    "#Check if raster (production) has all the info right\n",
    "!gdalinfo $prod_raster"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ea93e90c",
   "metadata": {},
   "source": [
    "## Production map  \n",
    "Process is:  \n",
    "- Open and visualize raster data\n",
    "- Transform to H3 (resolution 6) geodataframe\n",
    "- Compare mean and std. dev. of raster and H3 values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "2e0c25fa",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'driver': 'GTiff', 'dtype': 'float32', 'nodata': 0.0, 'width': 4320, 'height': 1668, 'count': 1, 'crs': CRS.from_epsg(4326), 'transform': Affine(0.08333334, 0.0, -179.99166665,\n",
      "       0.0, -0.08333334, 83.097781811), 'tiled': False, 'interleave': 'band'}\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "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>h3index</th>\n",
       "      <th>value</th>\n",
       "      <th>geometry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>595641277383442431</td>\n",
       "      <td>314.101593</td>\n",
       "      <td>POLYGON ((74.77739 30.52301, 74.80568 30.28560...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>595557989377638399</td>\n",
       "      <td>0.001375</td>\n",
       "      <td>POLYGON ((80.85161 26.74488, 80.85750 26.51828...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>596176000811794431</td>\n",
       "      <td>147.633499</td>\n",
       "      <td>POLYGON ((76.37923 15.72577, 76.38236 15.96687...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>595649583850192895</td>\n",
       "      <td>3434.707764</td>\n",
       "      <td>POLYGON ((70.43899 21.79230, 70.47752 21.58064...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>595540775148716031</td>\n",
       "      <td>4.291802</td>\n",
       "      <td>POLYGON ((81.92629 17.23684, 81.93420 17.48327...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              h3index        value  \\\n",
       "0  595641277383442431   314.101593   \n",
       "1  595557989377638399     0.001375   \n",
       "2  596176000811794431   147.633499   \n",
       "3  595649583850192895  3434.707764   \n",
       "4  595540775148716031     4.291802   \n",
       "\n",
       "                                            geometry  \n",
       "0  POLYGON ((74.77739 30.52301, 74.80568 30.28560...  \n",
       "1  POLYGON ((80.85161 26.74488, 80.85750 26.51828...  \n",
       "2  POLYGON ((76.37923 15.72577, 76.38236 15.96687...  \n",
       "3  POLYGON ((70.43899 21.79230, 70.47752 21.58064...  \n",
       "4  POLYGON ((81.92629 17.23684, 81.93420 17.48327...  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "with rio.open(prod_raster) as src:\n",
    "    window = rio.windows.from_bounds(*test_area, src.transform)\n",
    "    transform = rio.windows.transform(window, src.transform)\n",
    "    print(src.profile)\n",
    "    rio.plot.show(src.read(window=window, masked=True))\n",
    "    gdf = raster.raster_to_geodataframe(src.read(1, window=window), transform, h3_resolution=4, nodata_value=int(src.profile['nodata']), compacted=False)\n",
    "\n",
    "gdf.plot('value')\n",
    "gdf.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1bbc34a2",
   "metadata": {},
   "source": [
    "Calculate mean and standard deviation values for the raster data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "b88164fb",
   "metadata": {},
   "outputs": [],
   "source": [
    "src=rio.open(prod_raster)\n",
    "window = rio.windows.from_bounds(*test_area, src.transform)\n",
    "\n",
    "array=src.read(window=window)\n",
    "prod_df=array[0].ravel()\n",
    "rst_m = round(prod_df[prod_df > 0].mean(), 2)\n",
    "rst_s = round(prod_df[prod_df > 0].std(), 2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "060214a4",
   "metadata": {},
   "source": [
    "And for the H3 data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "93bcdd09",
   "metadata": {},
   "outputs": [],
   "source": [
    "h3_m = round(gdf['value'].mean(), 2)\n",
    "h3_s = round(gdf['value'].std(), 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "4be95297",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Raster PRODUCTION mean value: 144.52  std. dev.:521.12\n",
      "H3 map PRODUCTION mean value: 150.94  std. dev.:560.04\n"
     ]
    }
   ],
   "source": [
    "print(f'Raster PRODUCTION mean value: {rst_m:.2f}  std. dev.:{rst_s:.2f}')\n",
    "print(f'H3 map PRODUCTION mean value: {h3_m}  std. dev.:{h3_s}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8b29407f",
   "metadata": {},
   "source": [
    "## Risk map"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "a8415e7e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'driver': 'GTiff', 'dtype': 'float32', 'nodata': 0.0, 'width': 4320, 'height': 1668, 'count': 1, 'crs': CRS.from_epsg(4326), 'transform': Affine(0.08333334, 0.0, -179.99166664999998,\n",
      "       0.0, -0.08333334, 83.08834447000001), 'tiled': False, 'interleave': 'band'}\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "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>h3index</th>\n",
       "      <th>value</th>\n",
       "      <th>geometry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>596167376517464063</td>\n",
       "      <td>0.005649</td>\n",
       "      <td>POLYGON ((76.91709 10.26087, 76.92060 10.50475...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>595652616097103871</td>\n",
       "      <td>0.000758</td>\n",
       "      <td>POLYGON ((69.95694 24.38554, 69.99855 24.16575...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>595532159444320255</td>\n",
       "      <td>0.299519</td>\n",
       "      <td>POLYGON ((84.98547 26.64909, 84.99640 26.88171...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>595559630055145471</td>\n",
       "      <td>0.952000</td>\n",
       "      <td>POLYGON ((76.40268 29.23269, 76.42464 28.99841...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>595651843002990591</td>\n",
       "      <td>0.000818</td>\n",
       "      <td>POLYGON ((69.05924 24.26639, 69.10366 24.04729...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              h3index     value  \\\n",
       "0  596167376517464063  0.005649   \n",
       "1  595652616097103871  0.000758   \n",
       "2  595532159444320255  0.299519   \n",
       "3  595559630055145471  0.952000   \n",
       "4  595651843002990591  0.000818   \n",
       "\n",
       "                                            geometry  \n",
       "0  POLYGON ((76.91709 10.26087, 76.92060 10.50475...  \n",
       "1  POLYGON ((69.95694 24.38554, 69.99855 24.16575...  \n",
       "2  POLYGON ((84.98547 26.64909, 84.99640 26.88171...  \n",
       "3  POLYGON ((76.40268 29.23269, 76.42464 28.99841...  \n",
       "4  POLYGON ((69.05924 24.26639, 69.10366 24.04729...  "
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "risk_raster = '../../datasets/processed/h3_test/wr_cotton_india.tif'\n",
    "\n",
    "with rio.open(risk_raster) as src:\n",
    "    window = rio.windows.from_bounds(*test_area, src.transform)\n",
    "    transform = rio.windows.transform(window, src.transform)\n",
    "    print(src.profile)\n",
    "    rio.plot.show(src.read(window=window, masked=True))\n",
    "    gdf = raster.raster_to_geodataframe(src.read(1, window=window), transform, h3_resolution=4, nodata_value=int(src.profile['nodata']), compacted=False)\n",
    "\n",
    "gdf.plot('value')\n",
    "#gdf['h3index'] = gdf['h3index'].apply(hex)\n",
    "gdf.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "e454f4e7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Raster RISK mean value: 0.161  std. dev.:0.222\n",
      "H3 map RISK mean value: 0.165  std. dev.:0.222\n"
     ]
    }
   ],
   "source": [
    "src=rio.open(risk_raster)\n",
    "window = rio.windows.from_bounds(*test_area, src.transform)\n",
    "\n",
    "array=src.read(window=window)\n",
    "risk_df=array[0].ravel()\n",
    "rst_m = round(risk_df[risk_df > 0].mean(), 3)\n",
    "rst_s = round(risk_df[risk_df > 0].std(), 3)\n",
    "\n",
    "h3_m = round(gdf['value'].mean(), 3)\n",
    "h3_s = round(gdf['value'].std(), 3)\n",
    "\n",
    "print(f'Raster RISK mean value: {rst_m:.3f}  std. dev.:{rst_s:.3f}')\n",
    "print(f'H3 map RISK mean value: {h3_m}  std. dev.:{h3_s}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3058b022",
   "metadata": {},
   "source": [
    "## Impact map"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "76184415",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'driver': 'GTiff', 'dtype': 'float32', 'nodata': 0.0, 'width': 4320, 'height': 1668, 'count': 1, 'crs': CRS.from_epsg(4326), 'transform': Affine(0.08333334, 0.0, -179.99166665,\n",
      "       0.0, -0.08333334, 83.097781811), 'tiled': False, 'interleave': 'band'}\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "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>h3index</th>\n",
       "      <th>value</th>\n",
       "      <th>geometry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>595541041436688383</td>\n",
       "      <td>0.000001</td>\n",
       "      <td>POLYGON ((81.74298 18.34293, 81.75076 18.58794...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>596191316665171967</td>\n",
       "      <td>0.000064</td>\n",
       "      <td>POLYGON ((79.01816 12.43928, 79.02347 12.68569...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>595541445163614207</td>\n",
       "      <td>0.000005</td>\n",
       "      <td>POLYGON ((84.66632 19.44482, 84.67670 19.69096...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>596174282824876031</td>\n",
       "      <td>0.002683</td>\n",
       "      <td>POLYGON ((78.10682 19.37491, 78.11146 19.61452...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>595559664414883839</td>\n",
       "      <td>0.016826</td>\n",
       "      <td>POLYGON ((76.12492 29.56532, 76.14798 29.33020...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              h3index     value  \\\n",
       "0  595541041436688383  0.000001   \n",
       "1  596191316665171967  0.000064   \n",
       "2  595541445163614207  0.000005   \n",
       "3  596174282824876031  0.002683   \n",
       "4  595559664414883839  0.016826   \n",
       "\n",
       "                                            geometry  \n",
       "0  POLYGON ((81.74298 18.34293, 81.75076 18.58794...  \n",
       "1  POLYGON ((79.01816 12.43928, 79.02347 12.68569...  \n",
       "2  POLYGON ((84.66632 19.44482, 84.67670 19.69096...  \n",
       "3  POLYGON ((78.10682 19.37491, 78.11146 19.61452...  \n",
       "4  POLYGON ((76.12492 29.56532, 76.14798 29.33020...  "
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "impact_raster = '../../datasets/processed/h3_test/water_impact_cotton_ind_m3yr.tif'\n",
    "\n",
    "with rio.open(impact_raster) as src:\n",
    "    window = rio.windows.from_bounds(*test_area, src.transform)\n",
    "    transform = rio.windows.transform(window, src.transform)\n",
    "    print(src.profile)\n",
    "    rio.plot.show(src.read(window=window, masked=True))\n",
    "    gdf = raster.raster_to_geodataframe(src.read(1, window=window), transform, h3_resolution=4, nodata_value=int(src.profile['nodata']), compacted=False)\n",
    "\n",
    "gdf.plot('value')\n",
    "#gdf['h3index'] = gdf['h3index'].apply(hex)\n",
    "gdf.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "bbf13efa",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Raster IMPACT mean value: 0.008  std. dev.:0.038\n",
      "H3 map IMPACT mean value: 0.01   std. dev.:0.047\n"
     ]
    }
   ],
   "source": [
    "src=rio.open(impact_raster)\n",
    "window = rio.windows.from_bounds(*test_area, src.transform)\n",
    "\n",
    "array=src.read(window=window)\n",
    "impact_df=array[0].ravel()\n",
    "rst_m = round(impact_df[impact_df > 0].mean(), 3)\n",
    "rst_s = round(impact_df[impact_df > 0].std(), 3)\n",
    "\n",
    "h3_m = round(gdf['value'].mean(), 3)\n",
    "h3_s = round(gdf['value'].std(), 3)\n",
    "\n",
    "print(f'Raster IMPACT mean value: {rst_m:.3f}  std. dev.:{rst_s:.3f}')\n",
    "print(f'H3 map IMPACT mean value: {h3_m}   std. dev.:{h3_s}')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e2cad6d9",
   "metadata": {},
   "source": [
    "## Convert rasters to H3"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0ab0c9c0",
   "metadata": {},
   "source": [
    "### Production"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "0f848b2a",
   "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>value</th>\n",
       "      <th>geometry</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>h3index</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>8642d96dfffffff</th>\n",
       "      <td>1.278079</td>\n",
       "      <td>POLYGON ((73.54336 20.53316, 73.54348 20.56626...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86609928fffffff</th>\n",
       "      <td>1.278079</td>\n",
       "      <td>POLYGON ((73.57195 20.48526, 73.57206 20.51838...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86609929fffffff</th>\n",
       "      <td>1.278079</td>\n",
       "      <td>POLYGON ((73.60089 20.53672, 73.60101 20.56983...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8660aa80fffffff</th>\n",
       "      <td>77.621231</td>\n",
       "      <td>POLYGON ((75.33760 16.52547, 75.33792 16.55962...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8660aa857ffffff</th>\n",
       "      <td>77.621231</td>\n",
       "      <td>POLYGON ((75.27954 16.52388, 75.27986 16.55801...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>863c80a87ffffff</th>\n",
       "      <td>1.280013</td>\n",
       "      <td>POLYGON ((84.29110 21.23015, 84.29255 21.26491...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>863c80a8fffffff</th>\n",
       "      <td>1.280013</td>\n",
       "      <td>POLYGON ((84.35548 21.23020, 84.35693 21.26496...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>863c80a9fffffff</th>\n",
       "      <td>1.280013</td>\n",
       "      <td>POLYGON ((84.32112 21.17803, 84.32257 21.21280...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>863c1bc17ffffff</th>\n",
       "      <td>11.746679</td>\n",
       "      <td>POLYGON ((87.93091 25.29243, 87.93284 25.32638...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>863c1bca7ffffff</th>\n",
       "      <td>11.746679</td>\n",
       "      <td>POLYGON ((87.89471 25.24182, 87.89663 25.27578...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>68602 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                     value                                           geometry\n",
       "h3index                                                                      \n",
       "8642d96dfffffff   1.278079  POLYGON ((73.54336 20.53316, 73.54348 20.56626...\n",
       "86609928fffffff   1.278079  POLYGON ((73.57195 20.48526, 73.57206 20.51838...\n",
       "86609929fffffff   1.278079  POLYGON ((73.60089 20.53672, 73.60101 20.56983...\n",
       "8660aa80fffffff  77.621231  POLYGON ((75.33760 16.52547, 75.33792 16.55962...\n",
       "8660aa857ffffff  77.621231  POLYGON ((75.27954 16.52388, 75.27986 16.55801...\n",
       "...                    ...                                                ...\n",
       "863c80a87ffffff   1.280013  POLYGON ((84.29110 21.23015, 84.29255 21.26491...\n",
       "863c80a8fffffff   1.280013  POLYGON ((84.35548 21.23020, 84.35693 21.26496...\n",
       "863c80a9fffffff   1.280013  POLYGON ((84.32112 21.17803, 84.32257 21.21280...\n",
       "863c1bc17ffffff  11.746679  POLYGON ((87.93091 25.29243, 87.93284 25.32638...\n",
       "863c1bca7ffffff  11.746679  POLYGON ((87.89471 25.24182, 87.89663 25.27578...\n",
       "\n",
       "[68602 rows x 2 columns]"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prod_raster = '../../datasets/processed/h3_test/cotton_production_ind.tif'\n",
    "with rio.open(prod_raster) as src:\n",
    "    window = rio.windows.from_bounds(*test_area, src.transform)\n",
    "    transform = rio.windows.transform(window, src.transform)\n",
    "\n",
    "    gdf = raster.raster_to_geodataframe(src.read(1, window=window), transform, h3_resolution=6, nodata_value=int(src.profile['nodata']), compacted=False)\n",
    "\n",
    "# Cast 'h3index' numeric value as hexadecimal value and set as index\n",
    "gdf['h3index'] = gdf['h3index'].apply(lambda x: hex(x)[2:])\n",
    "gdf.index = gdf['h3index']\n",
    "gdf.drop(columns='h3index', inplace=True)\n",
    "gdf"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6be69822",
   "metadata": {},
   "source": [
    "Save to GeoJSON"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "37690ba4",
   "metadata": {},
   "outputs": [],
   "source": [
    "gdf.to_file('../../datasets/processed/h3_test/cotton_production_ind.geojson', driver=\"GeoJSON\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4e2a9bcc",
   "metadata": {},
   "source": [
    "Save to CSV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "4c45cacc",
   "metadata": {},
   "outputs": [],
   "source": [
    "gdf.drop('geometry', axis=1).to_csv('../../datasets/processed/h3_test/cotton_production_ind.csv', index=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "82433846",
   "metadata": {},
   "source": [
    "### Risk"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "19794a92",
   "metadata": {},
   "outputs": [],
   "source": [
    "risk_raster = '../../datasets/processed/h3_test/wr_cotton_india.tif'\n",
    "with rio.open(risk_raster) as src:\n",
    "    window = rio.windows.from_bounds(*test_area, src.transform)\n",
    "    transform = rio.windows.transform(window, src.transform)\n",
    "\n",
    "    gdf = raster.raster_to_geodataframe(src.read(1, window=window), transform, h3_resolution=6, nodata_value=int(src.profile['nodata']), compacted=False)\n",
    "\n",
    "# Cast 'h3index' numeric value as hexadecimal value and set as index\n",
    "gdf['h3index'] = gdf['h3index'].apply(lambda x: hex(x)[2:])\n",
    "gdf.index = gdf['h3index']\n",
    "gdf.drop(columns='h3index', inplace=True)\n",
    "\n",
    "gdf.to_file('../../datasets/processed/h3_test/wr_cotton_india.geojson', driver=\"GeoJSON\")\n",
    "gdf.drop('geometry', axis=1).to_csv('../../datasets/processed/h3_test/wr_cotton_india.csv', index=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cd2746a9",
   "metadata": {},
   "source": [
    "### Impact"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "083f9a0c",
   "metadata": {},
   "outputs": [],
   "source": [
    "impact_raster = '../../datasets/processed/h3_test/water_impact_cotton_ind_m3yr.tif'\n",
    "with rio.open(impact_raster) as src:\n",
    "    window = rio.windows.from_bounds(*test_area, src.transform)\n",
    "    transform = rio.windows.transform(window, src.transform)\n",
    "\n",
    "    gdf = raster.raster_to_geodataframe(src.read(1, window=window), transform, h3_resolution=6, nodata_value=int(src.profile['nodata']), compacted=False)\n",
    "\n",
    "# Cast 'h3index' numeric value as hexadecimal value and set as index\n",
    "gdf['h3index'] = gdf['h3index'].apply(lambda x: hex(x)[2:])\n",
    "gdf.index = gdf['h3index']\n",
    "gdf.drop(columns='h3index', inplace=True)\n",
    "\n",
    "gdf.to_file('../../datasets/processed/h3_test/water_impact_cotton_ind_m3yr.geojson', driver=\"GeoJSON\")\n",
    "gdf.drop('geometry', axis=1).to_csv('../../datasets/processed/h3_test/water_impact_cotton_ind_m3yr.csv', index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7748d54b",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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