{
    "info": {
        "author": "Joseph Stachelek",
        "author_email": "stachel2@msu.edu",
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            "License :: OSI Approved :: MIT License",
            "Operating System :: OS Independent",
            "Programming Language :: Python :: 3"
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        "description": "# gssurgo\n\n[![PyPiVersion](https://img.shields.io/pypi/v/gssurgo.svg)](https://pypi.python.org/pypi/gssurgo/) [![Project Status: Active - The project has reached a stable, usable state and is being actively developed.](http://www.repostatus.org/badges/latest/active.svg)](http://www.repostatus.org/#active) [![PYPI Downloads](https://img.shields.io/pypi/dm/gssurgo.svg)](https://pypistats.org/packages/gssurgo) [![Build Status](https://travis-ci.org/jsta/gssurgo.svg?branch=master)](https://travis-ci.org/jsta/gssurgo)\n\nThe `gssurgo` python package enables open source workflows with the `gSSURGO` dataset. It provides:\n\n* 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.**  \n\n* Python functions for converting Geodatabase files to geopackage format. \n\n* Python functions for returning the results of specific `SQL` queries of `gSSURGO` data.\n\n* Python functions for referencing query results to corresponding (raster) grid cells.\n\n## Prereqs\n\n* 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](bin/extract_gssurgo_tif) to enable alternate locations.\n\n* Remaining operations require the dependencies listed in [environment.yml](environment.yml) and [requirements.txt](requirements.txt). If using Anaconda, make sure you have the **64bit** version. You can install an Anaconda virtual environment with:\n\n```\nconda env create -n gssurgo -f environment.yml\nsource activate gssurgo\n```\n\n## Installation\n\n```\n# local install\n# pip install -e  . \n\n# development install \npip install git+git://github.com/jsta/gssurgo.git\n\n# development upgrade\n# pip install --upgrade git+git://github.com/jsta/gssurgo.git\n```\n\n## Usage\n\nA demonstration workflow using the `gssurgo` python package can be found at: https://github.com/jsta/gssurgo_data\n\n### 1. Extract tif and build gpkgs\n\n```\nextract_gssurgo_tif 'path/to/gSSURGO_STATE.gdb/MapunitRaster_10m' 'path/to/STATE.tif'\n```\n\n```py\nimport gssurgo\ngssurgo.build_gpkg(\"path/to/gSSURGO_STATE.gdb\", \"path/to/gSSURGO_STATE.gpkg\")\n```\n\n### 2. Generate an Area of Interest (AOI)\n\n```py\ngssurgo.aoi(in_raster_path = \"tifs\", out_raster = \"path/to/aoi.tif\", xmax = -88.34945, xmin = -88.35470, ymin = 38.70095, ymax = 38.70498)\n```\n\n### 3. Pull specific variable and merge with corresponding tif\n\n```py\ngssurgo.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\")\n\ngssurgo.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\")\n\n```\n\n> The `sql_query` parameter must give a two column result of `mukey` and `some_variable` where no `mukey` entries are duplicated.\n\n### 4. Visualize output\n\n```py\ngssurgo.viz_numeric_output(\"path/to/aoi_results.tif\", \"path/to/aoi_results.png\")\n```\n\n![](scratch/nonirryield_r.png)\n\n\n",
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