Metadata-Version: 2.1
Name: gssurgo
Version: 1.0.1
Summary: Python toolbox enabling an open source gSSURGO workflow
Home-page: https://github.com/jsta/gssurgo
Author: Jemma Stachelek
Author-email: stachel2@msu.edu
License: UNKNOWN
Description: # gssurgo
        
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        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](bin/extract_gssurgo_tif) to enable alternate locations.
        
        * 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:
        
        ```
        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'
        ```
        
        ```py
        import gssurgo
        gssurgo.build_gpkg("path/to/gSSURGO_STATE.gdb", "path/to/gSSURGO_STATE.gpkg")
        ```
        
        ### 2. Generate an Area of Interest (AOI)
        
        ```py
        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
        
        ```py
        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
        
        ```py
        gssurgo.viz_numeric_output("path/to/aoi_results.tif", "path/to/aoi_results.png")
        ```
        
        ![](scratch/nonirryield_r.png)
        
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Description-Content-Type: text/markdown
