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https://github.com/jsta/gssurgo
Python package enabling an open source gSSURGO workflow
https://github.com/jsta/gssurgo
python ssurgo
Last synced: about 2 months ago
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Python package enabling an open source gSSURGO workflow
- Host: GitHub
- URL: https://github.com/jsta/gssurgo
- Owner: jsta
- License: mit
- Created: 2018-07-26T14:13:40.000Z (over 6 years ago)
- Default Branch: master
- Last Pushed: 2022-01-06T23:18:44.000Z (about 3 years ago)
- Last Synced: 2024-10-10T09:35:53.531Z (4 months ago)
- Topics: python, ssurgo
- Language: Python
- Homepage: https://www.nrcs.usda.gov/wps/portal/nrcs/detail/soils/survey/geo/?cid=nrcs142p2_053628
- Size: 3.64 MB
- Stars: 9
- Watchers: 2
- Forks: 1
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
- Changelog: CHANGELOG
- License: LICENSE
Awesome Lists containing this project
README
# gssurgo
[](https://pypi.python.org/pypi/gssurgo/) [](http://www.repostatus.org/#active) [](https://pypistats.org/packages/gssurgo) [](https://travis-ci.org/jsta/gssurgo) [](https://zenodo.org/badge/latestdoi/142450474)
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")
```
