wcps 0.2.0

Python client library for WCPS (OGC Web Coverage Processing Service) backends.

Overview

The OGC Web Coverage Processing Service (WCPS) standard defines a protocol-independent declarative query language for the extraction, processing, and analysis of multi-dimensional coverages (datacubes) representing sensor, image, or statistics data.

This Python library allows to dynamically build WCPS queries and execute on a WCPS server.

Installation

pip install wcps

Examples

Derive an NDVI map from red and near-infrared bands of a Sentinel-2 datacube, threshold the very green areas (values greater than 0.5) as true values (white in a PNG), and save the result as a PNG image.

from wcps.service import Service
from wcps.model import Datacube

# Let S2_L2A be a 3D coverage with nir and red bands;
# slice it at "2025-01-15" on the time axis, getting a 2D map
cov = Datacube("S2_L2A")["time", "2025-01-15"]
# NDVI formula
ndvi = (cov.nir - cov.red) / (cov.nir + cov.red)
# threshold NDVI values to highlight areas with high vegetation
vegetation = ndvi > 0.5
# encode final result to PNG
query = vegetation.encode("PNG")

service = Service("https://ows.rasdaman.org/rasdaman/ows")

# execute the query on the server and get back the response
result = service.execute(query)

# similar to above, but automatically convert the PNG result 
# to a numpy array
result = service.execute(query, convert_to_numpy=True)

# alternatively, save the content of the response into a file
service.download(query, output_file='vegetation.png')

We can calculate the average NDVI as follows:

cov = ...
# NDVI formula
ndvi = (cov.nir - cov.red) / (cov.nir + cov.red)
# get average NDVI value
query = ndvi.avg()

service = ...
result = service.execute(query)

print(f'The average NDVI is {result.value}')

A more advanced expression is the general condenser (aggregation) operation. The example calculates the maximum values across all time slices from a 3D datacube between "2015-01-01" and "2015-07-01" for which the average is greater than 20:

from wcps.model import Datacube, AxisIter, Condense, CondenseOp
from wcps.service import Service

cov = Datacube("AvgTemperatureColorScaled")
ansi_iter = (AxisIter("ansi_iter", "ansi")
             .of_geo_axis(cov["ansi" : "2015-01-01" : "2015-07-01"]))
max_map = (Condense(CondenseOp.MAX)
           .over(ansi_iter)
           .where(cov["ansi": ansi_iter.ref()].avg() > 20)
           .using(cov["ansi": ansi_iter.ref()]))
query = max_map.encode("PNG")

service = Service("https://ows.rasdaman.org/rasdaman/ows")
service.download(query, 'max_map.png')

Contributing

The directory structure is as follows:

• wcps - the main library code
• tests - testing code
• docs - documentation in reStructuredText format

Tests

To run the tests:

# install dependencies
pip install wcps[tests]

pytest

Documentation

To build the documentation:

# install dependencies
pip install wcps[docs]

cd docs
make html

The built documentation can be found in the docs/_build/html/ subdir.