Metadata-Version: 2.1
Name: rasteric
Version: 0.0.2b0
Summary: Python Geospatial Library
Home-page: https://github.com/tnmthai/geotran
Author: Thai Tran
Author-email: me@tnmthai.com
License: MIT
Project-URL: Bug Tracker, https://github.com/tnmthai/geotran/issues
Project-URL: repository, https://github.com/tnmthai/geotran
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.6
Description-Content-Type: text/markdown
License-File: LICENSE.txt

The Python Geospatial Library, Rasteric, is a comprehensive toolkit for geospatial data preprocessing, analysis, and modeling. It provides a variety of functions for transforming and manipulating geospatial data, including data normalization, resampling, filtering, and feature selection. It also offers a range of analytical techniques, such as spatial clustering, classification, and regression, as well as machine learning algorithms for predictive modeling.    

Rasteric is designed to work with a variety of geospatial data formats, including shapefiles, GeoJSON, and raster data. It also supports integration with popular geospatial tools and platforms, such as QGIS, GDAL, and ArcGIS.     

Rasteric is widely used in the geospatial industry, research, and open-source communities for developing geospatial applications, performing spatial analysis, and modeling complex geospatial phenomena.        

Example:     

```
from rasteric import raster
from matplotlib import pyplot


fig, (axr, axg, axb) = pyplot.subplots(1,3, figsize=(21,7))

raster.plot('T60GVV.tif',bands=[3],ax=axr, title="Red", cmap='Reds')
raster.plot('T60GVV.tif',bands=[2],ax=axg, title="Green", cmap="Greens")
raster.plot('T60GVV.tif',bands=[1],ax=axb, title="Blue", cmap="Blues")
![Alt text](image.png)

clip_raster_by_shp(raster_file, shapefile, output_file, epsg_code=2193)

(more)
