Netcdf Converter

Conversion service for netcdf4 files to cloud optimized geotiff. This repository contains the source code, unit test suite, and Jupyter notebook documentation.

Directory structure

📁
├── .📁 github
├── 📁 cmr
├── 📁 bin
├── 📁 docker
├── 📁 docs
├── 📁 net2cog
├── 📁 tests
├── CHANGELOG.md
├── CONTRIBUTING.md
├── LICENSE
├── README.md
├── poetry.lock
├── pyproject.toml
└── run_tests.sh

• .github - Contains CI/CD workflows and pull request template.
• cmr - Contains files for updating the service's CMR UMM-S profile
• bin - A directory containing utility scripts to build the service and test images. A script to extract the release notes for the most recent version, as contained in CHANGELOG.md is also in this directory.
• docker - A directory containing the Dockerfiles for the service and test images. It also contains service_version.txt, which contains the semantic version number of the library and service image. Update this file with a new version to trigger a release.
• docs - A directory containing NetCDF Converter Service documentation.
• example - Directory containing Jupyter notebook documentation
• net2cog - The directory containing Python source code for the net2cog Service. netcdf_convert_harmony.py contains the NetcdfConverterService class that is invoked by calls to the service.
• tests - Contains the pytest test suite.
• CHANGELOG.md - Contains a record of changes applied to each new release of the net2cog Service.
• CONTRIBUTING.md - Instructions on how to contribute to the repository.
• LICENSE - Required for distribution under NASA open-source approval. Details conditions for use, reproduction and distribution.
• README.md - This file, containing guidance on developing the library and service.
• poetry.lock - Python's Poetry dependency management system. This file plays a crucial role in ensuring reproducible and consistent de.
• pyproject.toml - Contains a list of Python packages needed to run the service.
• run_tests.sh - Script to manage Python environment, install dependencies, and run tests. The script can be used to build and run pytest both locally and within Docker container.

Developer Notes

Local development:

Local testing of service functionality is best achieved via a local instance of Harmony. Please see instructions there regarding creation of a local Harmony-In-A-Box instance.

Test in Docker:

This service utilises the Python pytest package to perform unit tests on classes and functions in the service. After local development is complete, and test have been updated, they can be run via:

$ ./bin/build-image
$ ./bin/build-test
$ ./bin/run-test

The run_tests.sh script will also generate a coverage report, rendered in HTML, and scan the code with pylint.

The unittest suite is run automatically via GitHub Actions as part of a GitHub "workflow". These workflows are defined in the .github/workflows directory.

Test locally:

$ ./run_tests.sh

The run_tests.sh script will also generate a coverage report, rendered in HTML, and scan the code with pylint.