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Software tools used in the management of PDS4 Sub-models (formerly known as PDS4 Data Dictionaries).

Project description

PDS4 Sub-Model Management

This repo contains tools and software needed for managing PDS4 Sub-Models (formerly known as Data Dictionaries)


Include any system-wide requirements (brew install, apt-get install, yum install, …) Python 3 should be used regardless as Python 2 reached end-of-life on January 1st, 2020.

User Quickstart

Install with:

pip install my_pds_module

If possible, make it so that your program works out of the box without any additional configuration—but see the Configuration section for details.

To execute, run:

(put your run commands here)

Code of Conduct

All users and developers of the NASA-PDS software are expected to abide by our Code of Conduct. Please read this to ensure you understand the expectations of our community.


To develop this project, use your favorite text editor, or an integrated development environment with Python support, such as PyCharm.


For information on how to contribute to NASA-PDS codebases please take a look at our Contributing guidelines.


Install in editable mode and with extra developer dependencies into your virtual environment of choice:

pip install --editable '.[dev]'

Configure the pre-commit hooks:

pre-commit install && pre-commit install -t pre-push


To isolate and be able to re-produce the environment for this package, you should use a Python Virtual Environment. To do so, run:

python -m venv venv

Then exclusively use venv/bin/python, venv/bin/pip, etc. (It is no longer recommended to use venv/bin/activate.)

If you have tox installed and would like it to create your environment and install dependencies for you run:

tox --devenv <name you'd like for env> -e dev

Dependencies for development are specified as the dev extras_require in setup.cfg; they are installed into the virtual environment as follows:

pip install --editable '.[dev]'

All the source code is in a sub-directory under src.

You should update the setup.cfg file with:

  • name of your module
  • license, default apache, update if needed
  • description
  • download url, when you release your package on github add the url here
  • keywords
  • classifiers
  • install_requires, add the dependencies of you package
  • extras_require, add the development Dependencies of your package
  • entry_points, when your package can be called in command line, this helps to deploy command lines entry points pointing to scripts in your package

For the packaging details, see as a reference.


It is convenient to use ConfigParser package to manage configuration. It allows a default configuration which can be overwritten by the user in a specific file in their environment. See

For example:

candidates = ['my_pds_module.ini', 'my_pds_module.ini.default']
found =


You should not use print()vin the purpose of logging information on the execution of your code. Depending on where the code runs these information could be redirected to specific log files.

To make that work, start each Python file with:

import logging

logger = logging.getLogger(__name__)

To log a message:"my message")

In your main routine, include:


to get a basic logging system configured.


The dev extras_require included in the template repo installs black, flake8 (plus some plugins), and mypy along with default configuration for all of them. You can run all of these (and more!) with:

tox -e lint

Code Style

So that your code is readable, you should comply with the PEP8 style guide. Our code style is automatically enforced in via black and flake8. See the Tooling section for information on invoking the linting pipeline.

❗Important note for template users❗ The included pre-commit configuration file executes flake8 (along with mypy) across the entire src folder and not only on changed files. If you're converting a pre-existing code base over to this template that may result in a lot of errors that you aren't ready to deal with.

You can instead execute flake8 only over a diff of the current changes being made by modifying the pre-commit entry line:

entry: git diff -u | flake8 --diff

Or you can change the pre-commit config so flake8 is only called on changed files which match a certain filtering criteria:

-   repo: local
    -   id: flake8
        name: flake8
        entry: flake8
        files: ^src/|tests/
        language: system

Recommended Libraries

Python offers a large variety of libraries. In PDS scope, for the most current usage we should use:

Library Usage
configparser manage and parse configuration files
argparse command line argument documentation and parsing
requests interact with web APIs
lxml read/write XML files
json read/write JSON files
pyyaml read/write YAML files
pystache generate files from templates

Some of these are built into Python 3; others are open source add-ons you can include in your requirements.txt.


This section describes testing for your package.

A complete "build" including test execution, linting (mypy, black, flake8, etc.), and documentation build is executed via:


Unit tests

Your project should have built-in unit tests, functional, validation, acceptance, etc., tests.

For unit testing, check out the unittest module, built into Python 3.

Tests objects should be in packages test modules or preferably in project 'tests' directory which mirrors the project package structure.

Our unit tests are launched with command:


If you want your tests to run automatically as you make changes start up pytest in watch mode with:


Integration/Behavioral Tests

One should use the behave package and push the test results to "testrail".

See an example in


Your project should use Sphinx to build its documentation. PDS' documentation template is already configured as part of the default build. You can build your projects docs with:

python build_sphinx

You can access the build files in the following directory relative to the project root:



pip install wheel
python sdist bdist_wheel


NASA PDS packages can publish automatically using the Roundup Action, which leverages GitHub Actions to perform automated continuous integration and continuous delivery. A default workflow that includes the Roundup is provided in the .github/workflows/unstable-cicd.yaml file. (Unstable here means an interim release.)

Manual Publication

Create the package:

python bdist_wheel

Publish it as a Github release.

Publish on PyPI (you need a PyPI account and configure $HOME/.pypirc):

pip install twine
twine upload dist/*

Or publish on the Test PyPI (you need a Test PyPI account and configure $HOME/.pypirc):

pip install twine
twine upload --repository testpypi dist/*


The template repository comes with our two "standard" CI/CD workflows, stable-cicd and unstable-cicd. The unstable build runs on any push to main (± ignoring changes to specific files) and the stable build runs on push of a release branch of the form release/<release version>. Both of these make use of our GitHub actions build step, Roundup. The unstable-cicd will generate (and constantly update) a SNAPSHOT release. If you haven't done a formal software release you will end up with a v0.0.0-SNAPSHOT release (see NASA-PDS/roundup-action#56 for specifics).

Project details

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