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added zonal statistics example and updated example mapping grid data …
…to vector data
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docs/html/_sources/examples/grid_to_vector_map.ipynb.txt
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,278 @@ | ||
| { | ||
| "cells": [ | ||
| { | ||
| "cell_type": "markdown", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "# Example - Zonal Statistics\n", | ||
| "This is useful in the case where you want to get regional statistics for a raster." | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": 1, | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "import geopandas as gpd\n", | ||
| "import numpy\n", | ||
| "import rioxarray\n", | ||
| "import xarray\n", | ||
| "\n", | ||
| "from geocube.api.core import make_geocube" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "markdown", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "## Load in the source raster data" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": 2, | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "#!wget https://prd-tnm.s3.amazonaws.com/StagedProducts/Elevation/13/IMG/USGS_NED_13_n42w091_IMG.zip" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": 3, | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "elevation = rioxarray.open_rasterio(\n", | ||
| " \"zip://USGS_NED_13_n42w091_IMG.zip/USGS_NED_13_n42w091_IMG.img\"\n", | ||
| ").squeeze().drop(\"band\")\n", | ||
| "elevation.name = \"elevation\"" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "markdown", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "## Create the data mask by rasterizing the unique ID of the vector data" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": 4, | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "ssurgo_data = gpd.read_file(\"../../test/test_data/input/soil_data_group.geojson\")\n", | ||
| "ssurgo_data = ssurgo_data.loc[ssurgo_data.hzdept_r==0]\n", | ||
| "# convert the key to group to the vector data to an integer as that is one of the\n", | ||
| "# best data types for this type of mapping. If your data is not integer,\n", | ||
| "# then consider using a mapping of your data to an integer with something\n", | ||
| "# like a categorical dtype.\n", | ||
| "ssurgo_data[\"mukey\"] = ssurgo_data.mukey.astype(int)" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": 5, | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "out_grid = make_geocube(\n", | ||
| " vector_data=ssurgo_data,\n", | ||
| " measurements=[\"mukey\"],\n", | ||
| " like=elevation, # ensure the data are on the same grid\n", | ||
| " fill=numpy.nan\n", | ||
| ")" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": 6, | ||
| "metadata": {}, | ||
| "outputs": [ | ||
| { | ||
| "data": { | ||
| "text/plain": [ | ||
| "<xarray.Dataset>\n", | ||
| "Dimensions: (x: 10812, y: 10812)\n", | ||
| "Coordinates:\n", | ||
| " * y (y) float64 42.0 42.0 42.0 42.0 42.0 ... 41.0 41.0 41.0 41.0\n", | ||
| " * x (x) float64 -91.0 -91.0 -91.0 -91.0 ... -90.0 -90.0 -90.0 -90.0\n", | ||
| " spatial_ref int64 0\n", | ||
| "Data variables:\n", | ||
| " mukey (y, x) float64 nan nan nan nan nan nan ... nan nan nan nan nan\n", | ||
| " elevation (y, x) float32 ...\n", | ||
| "Attributes:\n", | ||
| " grid_mapping: spatial_ref" | ||
| ] | ||
| }, | ||
| "execution_count": 6, | ||
| "metadata": {}, | ||
| "output_type": "execute_result" | ||
| } | ||
| ], | ||
| "source": [ | ||
| "# merge the two together\n", | ||
| "out_grid[\"elevation\"] = elevation\n", | ||
| "out_grid" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "markdown", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "### Get the elevation statistics of each region using the mask" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": 7, | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "grouped_elevation = out_grid.drop(\"spatial_ref\").groupby(out_grid.mukey)\n", | ||
| "grid_mean = grouped_elevation.mean().rename({\"elevation\": \"elevation_mean\"})\n", | ||
| "grid_min = grouped_elevation.min().rename({\"elevation\": \"elevation_min\"})\n", | ||
| "grid_max = grouped_elevation.max().rename({\"elevation\": \"elevation_max\"})\n", | ||
| "grid_std = grouped_elevation.std().rename({\"elevation\": \"elevation_std\"})" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": 8, | ||
| "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>elevation_mean</th>\n", | ||
| " <th>elevation_min</th>\n", | ||
| " <th>elevation_max</th>\n", | ||
| " <th>elevation_std</th>\n", | ||
| " </tr>\n", | ||
| " <tr>\n", | ||
| " <th>mukey</th>\n", | ||
| " <th></th>\n", | ||
| " <th></th>\n", | ||
| " <th></th>\n", | ||
| " <th></th>\n", | ||
| " </tr>\n", | ||
| " </thead>\n", | ||
| " <tbody>\n", | ||
| " <tr>\n", | ||
| " <td>198692.0</td>\n", | ||
| " <td>173.130920</td>\n", | ||
| " <td>169.394562</td>\n", | ||
| " <td>188.442505</td>\n", | ||
| " <td>3.307044</td>\n", | ||
| " </tr>\n", | ||
| " <tr>\n", | ||
| " <td>198714.0</td>\n", | ||
| " <td>175.061554</td>\n", | ||
| " <td>170.214157</td>\n", | ||
| " <td>179.716675</td>\n", | ||
| " <td>2.148920</td>\n", | ||
| " </tr>\n", | ||
| " <tr>\n", | ||
| " <td>198724.0</td>\n", | ||
| " <td>179.933060</td>\n", | ||
| " <td>178.237244</td>\n", | ||
| " <td>181.490387</td>\n", | ||
| " <td>0.628017</td>\n", | ||
| " </tr>\n", | ||
| " <tr>\n", | ||
| " <td>198750.0</td>\n", | ||
| " <td>176.188461</td>\n", | ||
| " <td>167.951233</td>\n", | ||
| " <td>190.138763</td>\n", | ||
| " <td>3.814724</td>\n", | ||
| " </tr>\n", | ||
| " <tr>\n", | ||
| " <td>198754.0</td>\n", | ||
| " <td>171.632187</td>\n", | ||
| " <td>167.610321</td>\n", | ||
| " <td>181.611298</td>\n", | ||
| " <td>2.996241</td>\n", | ||
| " </tr>\n", | ||
| " <tr>\n", | ||
| " <td>271425.0</td>\n", | ||
| " <td>167.973709</td>\n", | ||
| " <td>167.951233</td>\n", | ||
| " <td>168.476776</td>\n", | ||
| " <td>0.076759</td>\n", | ||
| " </tr>\n", | ||
| " <tr>\n", | ||
| " <td>271431.0</td>\n", | ||
| " <td>176.718338</td>\n", | ||
| " <td>170.258133</td>\n", | ||
| " <td>180.460220</td>\n", | ||
| " <td>2.732229</td>\n", | ||
| " </tr>\n", | ||
| " </tbody>\n", | ||
| "</table>\n", | ||
| "</div>" | ||
| ], | ||
| "text/plain": [ | ||
| " elevation_mean elevation_min elevation_max elevation_std\n", | ||
| "mukey \n", | ||
| "198692.0 173.130920 169.394562 188.442505 3.307044\n", | ||
| "198714.0 175.061554 170.214157 179.716675 2.148920\n", | ||
| "198724.0 179.933060 178.237244 181.490387 0.628017\n", | ||
| "198750.0 176.188461 167.951233 190.138763 3.814724\n", | ||
| "198754.0 171.632187 167.610321 181.611298 2.996241\n", | ||
| "271425.0 167.973709 167.951233 168.476776 0.076759\n", | ||
| "271431.0 176.718338 170.258133 180.460220 2.732229" | ||
| ] | ||
| }, | ||
| "execution_count": 8, | ||
| "metadata": {}, | ||
| "output_type": "execute_result" | ||
| } | ||
| ], | ||
| "source": [ | ||
| "zonal_stats = xarray.merge([grid_mean, grid_min, grid_max, grid_std])\n", | ||
| "zonal_stats.to_dataframe()" | ||
| ] | ||
| } | ||
| ], | ||
| "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": "3.6.7" | ||
| } | ||
| }, | ||
| "nbformat": 4, | ||
| "nbformat_minor": 4 | ||
| } |
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