Developer’s guide

The source code for OpenGHG is available on GitHub.

Setting up your computer

OpenGHG requires Python >= 3.7, so please install this before continuing further.

Virtual environments

It is recommended that you develop OpenGHG in a Python virtual environment. You can create a new environment in your home directory ~/venvs/openghg-devel by typing;

mkdir -p ~/venvs
python -m venv ~/venvs/openghg-devel

Feel free to place the environment in any directory you want.

Virtual environments provide sandboxes which make it easier to develop and test code. They also allow you to install Python modules without interfering with other Python installations.

You activate you environment by typing;

source ~/venvs/openghg-devel/bin/activate

This will update your shell so that all python commands (such as python, pip etc.) will use the virtual environment. You can deactivate the environment and return to the “standard” Python using;

deactivate

If you no longer want the environment then you can remove it using

rm -rf venvs/openghg-devel

Coding Style

OpenGHG is written in Python 3 (>= 3.7). We aim as much as possible to follow a PEP8 python coding style and recommend that developers install and use a linter such as flake8.

This code has to run on a wide variety of architectures, operating systems and machines - some of which don’t have any graphic libraries, so please be careful when adding a dependency.

With this in mind, we use the following coding conventions:

Naming

We follow a Python style naming convention.

  • Packages: lowercase, singleword

  • Classes: CamelCase

  • Methods: snake_case

  • Functions: snake_case

  • Variables: snake_case

  • Source Files: snake_case with a leading underscore

Functions or variables that are private should be named with a leading underscore. This prevents them from being prominantly visible in Python’s help and tab completion.

Modules

OpenGHG consists of the main module, e.g. openghg, plus a pack_and_doc.submodule module.

In addition, there is a pack_and_doc.scripts module which contains the code for the various command-line applications.

To make OpenGHG easy for new developers to understand, we have a set of rules that will ensure that only necessary public functions, classes and implementation details are exposed to the Python help system.

  • Module files containing implementation details are prefixed with an underscore, i.e. _parameters.py

  • Each module file contains an __all__ variable that lists the specific items that should be imported.

  • The package __init__.py can be used to safely expose the required functionality to the user with:

from module import *

This results in a clean API and documentation, with all extraneous information, e.g. external modules, hidden from the user. This is important when working interactively, since IPython and Jupyter do not respect the __all__ variable when auto-completing, meaning that the user will see a full list of the available names when hitting tab. When following the conventions above, the user will only be able to access the exposed names. This greatly improves the clarity of the package, allowing a new user to quickly determine the available functionality. Any user wishing expose further implementation detail can, of course, type an underscore to show the hidden names when searching.

Workflow

Feature branches

First make sure that you are on the development branch of OpenGHG:

git checkout devel

Now create and switch to a feature branch. This should be prefixed with feature, e.g.

git checkout -b feature-process

Testing

When working on your feature it is important to write tests to ensure that it does what is expected and doesn’t break any existing functionality. Tests should be placed inside the tests directory, creating an appropriately named sub-directory for any new submodules.

The test suite is intended to be run using pytest. When run, pytest searches for tests in all directories and files below the current directory, collects the tests together, then runs them. Pytest uses name matching to locate the tests. Valid names start or end with test, e.g.:

# Files:
test_file.py       file_test.py
# Functions:
def test_func():
   # code to perform tests...
   return

def func_test():
   # code to perform tests...
   return

We use the convention of test_* when naming files and functions.

Running tests

To run the full test suite, simply type:

pytest tests

To run tests for a specific sub-module, e.g.:

pytest tests/utils

To only run the unit tests in a particular file, e.g.:

pytest tests/test_integration.py

To run a specific unit tests in a particular file, e.g.:

pytest tests/test_read_variables.py::test_parameterset

To get more detailed information about each test, run pytests using the verbose flag, e.g.:

pytest -v

More details regarding how to invoke pytest can be found here.

Writing tests

Basics

Try to keep individual unit tests short and clear. Aim to test one thing, and test it well. Where possible, try to minimise the use of assert statements within a unit test. Since the test will return on the first failed assertion, additional contextual information may be lost.

Floating point comparisons

Make use of the approx function from the pytest package for performing floating point comparisons, e.g:

from pytest import approx

assert 0.1 + 0.2 == approx(0.3)

By default, the approx function compares the result using a relative tolerance of 1e-6. This can be changed by passing a keyword argument to the function, e.g:

assert 2 + 3 == approx(7, rel=2)
Skipping tests

If you are using test-driven development it might be desirable to write your tests before implementing the functionality, i.e. you are asserting what the output of a function should be, not how it should be implemented. In this case, you can make use of the pytest skip decorator to flag that a unit test should be skipped, e.g.:

@pytest.mark.skip(reason="Not yet implemented.")
def test_new_feature():
    # A unit test for an, as yet, unimplemented feature.
    ...
Parametrizing tests

Often it is desirable to run a test for a range of different input parameters. This can be achieved using the parametrize decorator, e.g.:

import pytest
from operator import mul

@pytest.mark.parametrize("x", [1, 2])
@pytest.mark.parametrize("y", [3, 4])
def test_mul(x, y):
    """ Test the mul function. """
    assert mul(x, y) == mul(y, x)

Here the function test_mul is parametrized with two parameters, x and y. By marking the test in this manner it will be executed using all possible parameter pairs (x, y), i.e. (1, 3), (1, 4), (2, 3), (2, 4).

Alternatively:

import pytest
from operator import sub
@pytest.mark.parametrize("x, y, expected",
                        [(1, 2, -1),
                         (7, 3,  4),
                         (21, 58, -37)])
def test_sub(x, y, expected):
    """ Test the sub function. """
    assert sub(x, y) == -sub(y, x) == expected

Here we are passing a list containing different parameter sets, with the names of the parameters matched against the arguments of the test function.

Testing exceptions

Pytest provides a way of testing your code for known exceptions. For example, suppose we had a function that raises an IndexError:

def indexError():
    """ A function that raises an IndexError. """
    a = []
    a[3]

We could then write a test to validate that the error is thrown as expected:

def test_indexError():
    with pytest.raises(IndexError):
        indexError()
Custom attributes

It’s possible to mark test functions with any attribute you like. For example:

@pytest.mark.slow
def test_slow_function():
    """ A unit test that takes a really long time. """
    ...

Here we have marked the test function with the attribute slow in order to indicate that it takes a while to run. From the command line it is possible to run or skip tests with a particular mark.

pytest mypkg -m "slow"        # only run the slow tests
pytest mypkg -m "not slow"    # skip the slow tests

The custom attribute can just be a label, as in this case, or could be your own function decorator.

Continuous integration and delivery

We use GitHub Actions to run a full continuous integration (CI) on all pull requests to devel and master, and all pushes to devel and master. We will not merge a pull request until all tests pass. We only accept pull requests to devel. We only allow pull requests from devel to master. In addition to CI, we also perform a build of the website on pushes to devel and tags to master. The website is versioned, so that old the docs for old versions of the code are always available.

Documentation

OpenGHG is fully documented using a combination of hand-written files (in the doc folder) and auto-generated api documentation created from NumPy style docstrings. See here for details. The documentation is automatically built using Sphinx whenever a commit is pushed to devel, which will then update this documentation.

To build the documentation locally you will first need to install some additional packages. If you haven’t yet installed the developer requirements install

pip install -r requirements-dev.txt

Next ensure you have pandoc installed. To do this follow the instructions here

Then move to the doc directory and run:

make

When finished, point your browser to build/html/index.html.

Committing

If you create new tests, please make sure that they pass locally before commiting. When happy, commit your changes, e.g.

git commit src/openghg/_new_feature.py tests/test_feature \
    -m "Implementation and test for new feature."

Remember that it is better to make small changes and commit frequently.

If your edits don’t change the OpenGHG source code e.g. fixing typos in the documentation, then please add ci skip to your commit message.

git commit -a -m "Updating docs [ci skip]"

This will avoid unnecessarily running the GitHub Actions, e.g. running all the tests and rebuilding the documentation of the OpenGHG package etc. GitHub actions are configured in the file .github/workflows/main.yaml).

Next, push your changes to the remote server:

# Push to the feature branch on the main OpenGHG repo, if you have access.
git push origin feature

# Push to the feature branch your own fork.
git push fork feature

When the feature is complete, create a pull request on GitHub so that the changes can be merged back into the development branch. For information, see the documentation here.