Contributing to modflow-setup

(Note: much of this page was cribbed from the geopandas project, which has similar guidelines to pandas and xarray.)

Getting started

All contributions, bug reports, bug fixes, documentation improvements, enhancements, and ideas are welcome. If an issue that interests you isn’t already listed in the Issues tab, consider filing an issue.

Bug reports and enhancement requests

Bug reports are an important part of improving Modflow-setup. Having a complete bug report will allow others to reproduce the bug and provide insight into fixing. See this stackoverflow article and this blogpost for tips on writing a good bug report.

Trying the bug-producing code out on the develop branch is often a worthwhile exercise to confirm the bug still exists. It is also worth searching existing bug reports and pull requests to see if the issue has already been reported and/or fixed.

To file a bug report or enhancement request, from the issues tab on the Modflow-setup GitHub page, select “New Issue”.

Bug reports must:

1. Include a short, self-contained Python snippet reproducing the problem, along with the contents of your configuration file and the full error traceback. You can format the code nicely by using GitHub Flavored Markdown:

   ```python
   >>> import mfsetup
   >>> m = MF6model.setup_from_yaml('pleasant_lgr_parent.yml')
   ```
   

   e.g.:

   ```yaml
   <paste configuration file contents here>
   ```
   
   ```python
   <paste error traceback here>
   ```
   

2. Include the version of Modflow-setup that you are running, which can be obtained with:

   import mfsetup
   mfsetup.__version__
   

   Depending on the issue, it may also be helpful to include information about the version(s) of python, key dependencies (e.g. numpy, pandas, etc) and operating system. You can get the versions of packages in a conda python environment with:

   conda list
   

3. Explain why the current behavior is wrong/not desired and what you expect instead.

The issue will then be visible on the Issues tab and open to comments/ideas from others.

Code contributions

Code contributions to Modflow-setup to fix bugs, implement new features or improve existing code are encouraged. Regardless of the context, consider filing an issue first to make others aware of the problem and allow for discussion on potential approaches to addressing it.

In general, Modflow-setup trys to follow the conventions of the pandas project where applicable. Contributions to Modflow-setup are likely to be accepted more quickly if they follow these guidelines.

In particular, when submitting a pull request:

• All existing tests should pass. Please make sure that the test suite passes, both locally and on GitHub Actions. Status with GitHub Actions will be visible on a pull request.

• New functionality should include tests. Please write reasonable tests for your code and make sure that they pass on your pull request.

• Classes, methods, functions, etc. should have docstrings. The first line of a docstring should be a standalone summary. Parameters and return values should be documented explicitly. (Note: there are admittedly more than a few places in the existing code where docstrings are missing. Docstring contributions are especially welcome!

• Follow PEP 8 when possible. For more details see below.

• Following the FloPy Commit Message Guidelines (which are similar to the Conventional Commits specification) is encouraged. Structured commit messages like these can result in more explicit commit messages that are more informative, and also facilitate automation of project maintenance tasks.

• Imports should be grouped with standard library imports first, 3rd-party libraries next, and modflow-setup imports third. Within each grouping, imports should be alphabetized. Always use absolute imports when possible, and explicit relative imports for local imports when necessary in tests. Imports can be sorted automatically using the isort package with a pre-commit hook. For more details see below.

• modflow-setup supports Python 3.7+ only.

Seven Steps for Contributing

There are seven basic steps to contributing to modflow-setup:

1. Fork the modflow-setup git repository

2. Create a development environment

3. Install modflow-setup dependencies

4. Installing the modflow-setup source code

5. Make changes to code and add tests

6. Update the documentation

7. Submit a Pull Request

Each of these 7 steps is detailed below.

1) Forking the modflow-setup repository using Git

To the new user, working with Git is one of the more daunting aspects of contributing to modflow-setup*. It can very quickly become overwhelming, but sticking to the guidelines below will help keep the process straightforward and mostly trouble free. As always, if you are having difficulties please feel free to ask for help.

The code is hosted on GitHub. To contribute you will need to sign up for a free GitHub account. We use Git for version control to allow many people to work together on the project.

Some great resources for learning Git:

• Software Carpentry’s Git Tutorial

• Atlassian

• the GitHub help pages.

• Matthew Brett’s Pydagogue.

Getting started with Git

GitHub has instructions for installing git, setting up your SSH key, and configuring git. All these steps need to be completed before you can work seamlessly between your local repository and GitHub.

Forking

You will need your own fork to work on the code. Go to the modflow-setup project page and hit the Fork button. You will want to clone your fork to your machine:

git clone git@github.com:your-user-name/modflow-setup.git modflow-setup-yourname
cd modflow-setup-yourname
git remote add upstream https://github.com/modflow-setup/modflow-setup.git

This creates the directory modflow-setup-yourname and connects your repository to the upstream (main project) modflow-setup repository.

The testing suite will run automatically on Travis-CI once your pull request is submitted. However, if you wish to run the test suite on a branch prior to submitting the pull request, then Travis-CI needs to be hooked up to your GitHub repository. Instructions for doing so are here.

Creating a branch

You want your master branch to reflect only production-ready code, so create a feature branch for making your changes. For example:

git branch shiny-new-feature
git checkout shiny-new-feature

The above can be simplified to:

git checkout -b shiny-new-feature

This changes your working directory to the shiny-new-feature branch. Keep any changes in this branch specific to one bug or feature so it is clear what the branch brings to modflow-setup. You can have many shiny-new-features and switch in between them using the git checkout command.

To update this branch, you need to retrieve the changes from the develop branch:

git fetch upstream
git rebase upstream/develop

This will replay your commits on top of the latest modflow-setup git develop. If this leads to merge conflicts, you must resolve these before submitting your pull request. It’s a good idea to move slowly while doing this and pay attention to the messages from git. The wrong command at the wrong time can quickly spiral into a confusing mess.

If you have uncommitted changes, you will need to stash them prior to updating. This will effectively store your changes and they can be reapplied after updating.

2 & 3) Creating a development environment with the required dependencies

A development environment is a virtual space where you can keep an independent installation of modflow-setup. This makes it easy to keep both a stable version of python in one place you use for work, and a development version (which you may break while playing with code) in another.

An easy way to create a modflow-setup development environment is as follows:

• Install either Anaconda or miniconda

• Make sure that you have cloned the repository

• cd to the modflow-setup* source directory

Tell conda to create a new environment, named modflow-setup_dev, that has all of the python packages needed to contribute to modflow-setup. Note that in the geopandas instructions, this step is broken into two parts- 2) creating the environment, and 3) installing the dependencies. By using a yaml file that includes the environment name and package requirements, these two steps can be combined:

conda env create -f requirements-dev.yml

This will create the new environment, and not touch any of your existing environments, nor any existing python installation.

To work in this environment, you need to activate it. The instructions below should work for both Windows, Mac and Linux:

conda activate modflow-setup_dev

Once your environment is activated, you will see a confirmation message to indicate you are in the new development environment.

To view your environments:

conda info -e

To return to your home root environment:

conda deactivate

See the full conda docs here.

At this point you can easily do a development install, as detailed in the next sections.

4) Installing the modflow-setup source code

Once dependencies are in place, install the modflow-setup source code by navigating to the git clone of the modflow-setup repository and (with the modflow-setup_dev environment activated) running:

pip install -e .

Note

Don’t forget the . after pip install -e!

5) Making changes and writing tests

modflow-setup is serious about testing and strongly encourages contributors to embrace test-driven development (TDD). This development process “relies on the repetition of a very short development cycle: first the developer writes an (initially failing) automated test case that defines a desired improvement or new function, then produces the minimum amount of code to pass that test.” So, before actually writing any code, you should write your tests. Often the test can be taken from the original GitHub issue. However, it is always worth considering additional use cases and writing corresponding tests.

In general, tests are required for code pushed to modflow-setup. Therefore, it is worth getting in the habit of writing tests ahead of time so this is never an issue.

modflow-setup uses the pytest testing system and the convenient extensions in numpy.testing and pandas.testing.

Writing tests

All tests should go into the tests directory. This folder contains many current examples of tests, and we suggest looking to these for inspiration. In general, the tests in this folder aim to be organized by module (e.g. test_lakes.py for the functions in lakes.py) or test case (e.g. test_mf6_shellmound.py for the Shellmound test case).

The .testing module has some special functions to facilitate writing tests. The easiest way to verify that your code is correct is to explicitly construct the result you expect, then compare the actual result to the expected correct result.

Running the test suite

The tests can then be run directly inside your Git clone (without having to install modflow-setup) by typing:

pytest

6) Updating the Documentation

The modflow-setup documentation resides in the docs folder. Changes to the docs are made by modifying the appropriate file in the source folder within docs. The modflow-setup docs use reStructuredText syntax, which is explained here and the docstrings follow the Numpy Docstring standard.

Once you have made your changes, you can try building the docs using sphinx. To do so, you can navigate to the doc folder and type:

make -C docs html

The resulting html pages will be located in docs/build/html. It’s a good practice to rebuild the docs often while writing to stay on top of any mistakes. The reStructuredText extension in VS Code is another way to continuously preview a rendered documentation page while writing.

7) Submitting a Pull Request

Once you’ve made changes and pushed them to your forked repository, you then submit a pull request to have them integrated into the modflow-setup code base.

You can find a pull request (or PR) tutorial in the GitHub’s Help Docs.

Style Guide & Linting

modflow-setup tries to follow the PEP8 standard. At this point, there’s no enforcement of this, but I am considering implementing Black, which automates a code style that is PEP8-complient. Many editors perform automatic linting that makes following PEP8 easy.

modflow-setup does use the isort package to automatically organize import statements. isort can installed via pip:

$ pip install isort

And then run with:

$ isort .

from the root level of the project.

Optionally (but recommended), you can setup pre-commit hooks to automatically run isort when you make a git commit. This can be done by installing pre-commit:

$ python -m pip install pre-commit

From the root of the modflow-setup repository, you should then install the pre-commit included in modflow-setup:

$ pre-commit install

Then isort will be run automatically each time you commit changes. You can skip these checks with git commit --no-verify.