geoserver-rest 1.0.1

Package for GeoServer rest API

Installation

The geoserver-rest package is useful for the management for geospatial data in GeoServer. The package is usefull for the creating, updating and deleting geoserver workspaces, stores, layers, and style files. For installation of this package following package should be installed first,

Dependencies

1. Gdal
2. Pycurl

The geoserver-rest package can be installed with pip, if all the dependencies are installed already

pip install geoserver-rest

How to use

This library is used for creating workspace, coveragestore, featurestore, styles. Some of the examples are shown below.

Initialize the library

This step is used to initialize the library. It takes parameters as geoserver url, username, password.

from geo.Geoserver import Geoserver
geo = Geoserver('http://localhost:8080/geoserver', username='admin', password='geoserver')

Create workspace

geo.create_workspace(workspace='demo')

Create coveragestore

It is helpful for publishing the raster data to the geoserver. Here if you didn't to pass the lyr_name parameter, it will take the raster file name as the layer name.

geo.create_coveragestore(lyr_name='layer1' path=r'path\to\raster\file.tif', workspace='demo')

If the layername already exist in geoserver you can pass another parameter overwrite=True,

geo.create_coveragestore(lyr_name='layer1' path=r'path\to\raster\file.tif', workspace='demo' overwrite=True)

Create featurestore and publish layer

It is use for connecting the PostGIS with geoserver and publish this as a layer. It is only useful for vector data. The postgres connection parameters should be pass in the parameters. For publish the PostGIS tables, the pg_table parameter represent the table name in postgres

geo.create_featurestore(store_name='geo_data', workspace='demo', db='postgres', host='localhost', pg_user='postgres', pg_password='admin')
geo.publish_featurestore(workspace='demo', store_name='geo_data', pg_table='geodata_table_name')

Upload style and publish it

It is use for uploading SLD files and publish style. If the style name already exist, you can pass the parameter overwrite=True to overwrite it. The name of the style will be name of the uploaded file name.

geo.upload_style(path=r'path\to\sld\file.sld', workspace='demo')
geo.publish_style(layer_name='geoserver_layer_name', style_name='sld_file_name', workspace='demo')

Create Coverage Style based on the raster (Dynamic) and apply style

It is use to create the style file for raster data. You can get the color_ramp name from matplotlib colormaps. By default color_ramp='RdYlGn' (red to green color ramp).

#Style name will be the same as the raster_file_name
geo.create_coveragestyle(raster_path=r'path\to\raster\file.tiff', style_name='style_1', workspace='demo', color_ramp='RdYiGn')
geo.publish_style(layer_name='geoserver_layer_name', style_name='raster_file_name', workspace='demo')

For generate the style for classified raster you can pass the another parameter called cmap_type='values' as,

geo.create_coveragestyle(raster_path=r'path\to\raster\file.tiff', style_name='style_1', workspace='demo', color_ramp='RdYiGn', cmap_type='values')

Option	Type	Default	Description
style_name	string	file_name	This is optional field. If you didn't pass the style_name parameter, then it will take the raster file name as the default name of style in geoserver
raster_path	path	None	path to the raster file
workspace	string	None	The name of the workspace
color_ramp	string	RdYlGn	The color ramp name. The name of the color ramp can be found here in matplotlib colormaps
cmap_type	string	ramp	By default the continuous style will be generated, If you want to generate the style for classified raster then pass the parameter color_ramp='values'
overwrite	boolean	False	For overwrite the previous style file in geoserver

Create featur style

It is use for creating the style for point, line, polygon dynamically. Currently it supports three different type of feature styles,

1. Outline featurestyle: For creating the style which have only boundary color not the fill style
2. Catagorized featurestyle: For creating catagorized dataset
3. Classified featurestyle: Classify the input data and style it: (For now only support for polygon geometry)

geo.create_outline_featurestyle(style_name='new_style' color="#3579b1" geom_type='polygon', workspace='demo')
geo.create_catagorized_featurestyle(style_name='name_of_style', column_name='name_of_column', column_distinct_values=[1,2,3,4,5,6,7], workspace='demo')
geo.create_classified_featurestyle(style_name='name_of_style' column_name='name_of_column', column_distinct_values=[1,2,3,4,5,6,7], workspace='demo')

Note:

• The geom_type must be point or line or polygon
• The color_ramp name can be get from matplotlib colormaps.

The options for creating catagorized/classified featurestyle are as follows,

Option	Type	Default	Description
style_name	string	None	The name of the style file in geoserver
column_name	string	None	The name of the column, based on which the style will be generated
column_distinct_values	list/array	None	The column distinct values based on which the style will be applied/classified
workspace	string	None	The name of the workspace
color_ramp	string	RdYlGn	The color ramp name. The name of the color ramp can be found here in matplotlib colormaps
geom_type	string	polygon	The geometry type, Available options are point, line, polygon .
outline_color	color hex value	'#3579b1'	The outline color of the polygon/line
overwrite	boolean	False	For overwrite the previous style file in geoserver

Some of the delete request examples

# delete workspace 
geo.delete_workspace(workspace='demo')

# delete layer
geo.delete_layer(layer_name='agri_final_proj', workspace='demo')

# delete feature store, i.e. remove postgresql connection
geo.delete_featurestore(featurestore_name='ftry', workspace='demo')

# delete coveragestore, i.e. delete raster store
geo.delete_coveragestore(coveragestore_name='agri_final_proj', workspace='demo')

# delete style file
geo.delete_style(style_name='kamal2', workspace='demo')

Automation process

The following code will first convert all the .rst data format inside C:\Users\gic\Desktop\etlIa\ folder, convert it to the tiff format and after that upload all the tiff files to the GeoServer.

from geo.Geoserver import Geoserver
from osgeo import gdal
import glob
import os

geo = Geoserver('http://localhost:8080/geoserver', username='admin', password='geoserver')

rst_files = glob.glob(r'C:\Users\gic\Desktop\etlIa\*.rst')
# geo.create_workspace('geonode')

for rst in rst_files:
    file_name = os.path.basename(file_name)
    src = gdal.Open(rst)
    tiff = r'C:\Users\tek\Desktop\try\{}'.format(file_name)
    gdal.Translate(tiff, src)
    geo.create_coveragestore(lyr_name=file_name, path=tiff, workspace='geonode')    #, overwrite=True

Acknowledgements

Created and managed by Tek Bahadur Kshetri for activites of Geoinformatics Center of Asian Institute of Technology, Thailand.