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Python toolbox enabling an open source gSSURGO workflow

Project description

gssurgo

PyPiVersion Project Status: Active - The project has reached a stable, usable state and is being actively developed. PYPI Downloads Build Status Code DOI

The gssurgo python package enables open source workflows with the gSSURGO dataset. It provides:

  • A shell script extract_gssurgo_tif for generating stand-alone gSSURGO grids. These raster grids are distributed within file geodatabase archives and can only be extracted using ArcGIS, the fileGDB driver, or (in the case of extract_gssurgo_tif) the arcpy python package.

  • Python functions for converting Geodatabase files to geopackage format.

  • Python functions for returning the results of specific SQL queries of gSSURGO data.

  • Python functions for referencing query results to corresponding (raster) grid cells.

Prereqs

  • The intial tif (grid) extraction step requies the arcpy python module. This step assumes that a python executable linked to arcpy can be found at C:\Python27\ArcGIS10.3\python.exe. Edit bin/extract_gssurgo_tif to enable alternate locations.

  • Remaining operations require the dependencies listed in environment.yml and requirements.txt. If using Anaconda, make sure you have the 64bit version. You can install an Anaconda virtual environment with:

conda env create -n gssurgo -f environment.yml
source activate gssurgo

Installation

# local install
# pip install -e  . 

# development install 
pip install git+git://github.com/jsta/gssurgo.git

# development upgrade
# pip install --upgrade git+git://github.com/jsta/gssurgo.git

Usage

A demonstration workflow using the gssurgo python package can be found at: https://github.com/jsta/gssurgo_data

1. Extract tif and build gpkgs

extract_gssurgo_tif 'path/to/gSSURGO_STATE.gdb/MapunitRaster_10m' 'path/to/STATE.tif'
import gssurgo
gssurgo.build_gpkg("path/to/gSSURGO_STATE.gdb", "path/to/gSSURGO_STATE.gpkg")

2. Generate an Area of Interest (AOI)

gssurgo.aoi(in_raster_path = "tifs", out_raster = "path/to/aoi.tif", xmax = -88.34945, xmin = -88.35470, ymin = 38.70095, ymax = 38.70498)

3. Pull specific variable and merge with corresponding tif

gssurgo.query_gpkg(src_tif = "tests/aoi.tif", gpkg_path = "path/to/gkpgs/", sql_query = 'SELECT mukey, nonirryield_r FROM mucropyld WHERE (cropname = "Corn")', out_raster = "tests/aoi_results.tif")

gssurgo.query_gpkg(src_tif = "tests/aoi.tif", gpkg_path = "path/to/gpkgs/", sql_query = 'SELECT mukey, nonirryield_r FROM mucropyld WHERE (cropname = "Corn")', out_raster = "tests/aoi_results.tif")

The sql_query parameter must give a two column result of mukey and some_variable where no mukey entries are duplicated.

4. Visualize output

gssurgo.viz_numeric_output("path/to/aoi_results.tif", "path/to/aoi_results.png")

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