Contributing

Note

This document merges ideas and guidelines from the xarray Contributing Guide, which in turn builds largely upon the Pandas Contributing Guide , and from the audreyr cookiecutter package template . Some of the guidelines are more aspirational than practical at this point and constitute an attempt to learn how to build a proper open source software repository.

Where to start?

All contributions, bug reports, bug fixes, documentation improvements, enhancements, and ideas are welcome.

If you are brand new to ctdproc or open-source development, we recommend going through the GitHub “issues” tab to find issues that interest you. There are a number of issues listed under Documentation and good first issue where you could start out. Once you’ve found an interesting issue, you can return here to get your development environment setup.

Bug reports and enhancement requests 

Bug reports are an important part of making ctdproc more stable. Having a complete bug report will allow others to reproduce the bug and provide insight into fixing. See this stackoverflow article for tips on writing a good bug report.

Trying the bug-producing code out on the master 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.

Bug reports must:

Include a short, self-contained Python snippet reproducing the problem. You can format the code nicely by using GitHub Flavored Markdown:
```
```python
>>> import ctdproc
>>> hexfile = 'path/to/data.hex'
>>> c = ctd.io.CTD(hexfile)
...
```
```

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

The issue will then show up to the ctdproc community and be open to comments/ideas from others.

Working with the code 

Now that you have an issue you want to fix, enhancement to add, or documentation to improve, you need to learn how to work with GitHub and the ctdproc code base.

Version control, Git, and GitHub 

To the new user, working with Git is one of the more daunting aspects of contributing to ctdproc. 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:

the GitHub help pages.
the NumPy’s documentation.
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 ctdproc project page and hit the Fork button. You will want to clone your fork to your machine:

git clone https://github.com/your-user-name/ctdproc.git
cd ctdproc
git remote add upstream https://github.com/gunnarvoet/ctdproc.git

This creates the directory ctdproc and connects your repository to the upstream (main project) ctdproc repository.

Creating a development environment 

To test out code changes, you’ll need to build ctdproc from source, which requires a Python environment.

Creating a Python Environment 

Before starting any development, you’ll need to create an isolated ctdproc development environment:

Install either Anaconda or miniconda
Make sure your conda is up to date (conda update conda)
Make sure that you have cloned the repository
cd to the ctdproc source directory

We’ll now kick off a two-step process:

Install the build dependencies
Build and install ctdproc

# Create and activate the build environment
# This is for Linux and MacOS. On Windows, use py37-windows.yml instead.
conda env create -f environment.yml

conda activate ctdproc

# or with older versions of Anaconda:
source activate ctdproc

# Build and install ctdproc
pip install -e .

At this point you should be able to import ctdproc from your locally built version:

$ python  # start an interpreter
>>> import ctdproc
>>> ctdproc.__version__
'0.1.3+dev46.g015daca'

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

To view your environments:

conda info -e

To return to your root environment:

conda deactivate

See the full conda docs here.

Creating a branch 

You want your main 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 ctdproc. 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 main branch:

git fetch upstream
git rebase upstream/main

This will replay your commits on top of the latest ctdproc git main. If this leads to merge conflicts, you must resolve these before submitting your pull request. If you have uncommitted changes, you will need to git stash them prior to updating. This will effectively store your changes and they can be reapplied after updating.

Contributing to the documentation 

If you’re not the developer type, contributing to the documentation is still of huge value. The documentation is written in reStructuredText, which is almost like writing in plain English, and built using Sphinx. The Sphinx Documentation has an excellent introduction to reST. Review the Sphinx docs to perform more complex changes to the documentation as well.

Some other important things to know about the docs:

The ctdproc documentation consists of two parts: the docstrings in the code itself and the docs in this folder ctdproc/docs/.

The docstrings are meant to provide a clear explanation of the usage of the individual functions, while the documentation in this folder consists of tutorial-like overviews per topic together with some other information (what’s new, installation, etc).
The docstrings follow the Numpy Docstring Standard, which is used widely in the Scientific Python community. This standard specifies the format of the different sections of the docstring. See this document for a detailed explanation, or look at some of the existing functions to extend it in a similar manner.

Navigate to your root ctdproc/ directory in the console and run:

make docs

Then you can find the HTML output in the folder ctdproc/docs/_build/html/. The newly built site will also automatically open in your browser.

Contributing to the code base 

Code standards 

Writing good code is not just about what you write. It is also about how you write it. During Continuous Integration testing, several tools will be run to check your code for stylistic errors. Generating any warnings will cause the test to fail. Thus, good style is a requirement for submitting code to ctdproc.

In addition, because other people may use our library, it is important that we do not make sudden changes to the code that could have the potential to break a lot of user code as a result, that is, we need it to be as backwards compatible as possible to avoid mass breakages.

Code Formatting 

ctdproc uses several tools to ensure a consistent code format throughout the project:

Black for standardized code formatting
Flake8 for general code quality
isort for standardized order in imports. See also flake8-isort.

pip:

pip install black flake8 isort mypy

and then run from the root of the ctdproc repository:

isort -rc .
black -t py36 .
flake8

to auto-format your code. Additionally, many editors have plugins that will apply black as you edit files.

Optionally, you may wish to setup pre-commit hooks to automatically run all the above tools every time you make a git commit. This can be done by installing pre-commit:

pip install pre-commit

and then running:

pre-commit install

from the root of the ctdproc repository. You can skip the pre-commit checks with git commit --no-verify.

Testing With Continuous Integration 

The ctdproc test suite runs automatically the Travis CI, continuous integration service, once your pull request is submitted.

A pull-request will be considered for merging when you have an all ‘green’ build. If any tests are failing, then you will get a red ‘X’, where you can click through to see the individual failed tests.

Note

Each time you push to your PR branch, a new run of the tests will be triggered on the CI.

Test-driven development/code writing 

ctdproc 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.

Like many packages, ctdproc uses pytest and the convenient extensions in numpy.testing.

Writing tests 

All tests should go into the tests subdirectory of the specific package. This folder contains current examples of tests, and we suggest looking to these for inspiration.

Here is an example of a self-contained set of tests that illustrate multiple features that we like to use.

functional style: tests are like test_* and only take arguments that are either fixtures or parameters
pytest.mark can be used to set metadata on test functions, e.g. skip or xfail.
using parametrize: allow testing of multiple cases
to set a mark on a parameter, pytest.param(..., marks=...) syntax should be used
fixture, code for object construction, on a per-test basis
using bare assert for scalars and truth-testing

We would name this file test_really_cool_feature.py and put in an appropriate place in the ctdproc/tests/ structure.

import pytest
import numpy as np


@pytest.mark.parametrize('dtype', ['int8', 'int16', 'int32', 'int64'])
def test_dtypes(dtype):
    assert str(np.dtype(dtype)) == dtype


@pytest.mark.parametrize('dtype', ['float32',
                         pytest.param('int16', marks=pytest.mark.skip),
                         pytest.param('int32', marks=pytest.mark.xfail(
                            reason='to show how it works'))])
def test_mark(dtype):
    assert str(np.dtype(dtype)) == 'float32'


@pytest.fixture
def fake_data():
    return np.array([1, 2, 3])


@pytest.fixture(params=['int8', 'int16', 'int32', 'int64'])
def dtype(request):
    return request.param


def test_series(fake_data, dtype):
    result = fake_data.astype(dtype)
    assert result.dtype == dtype

A test run of this yields

(ctdproc) $ pytest ctdproc/tests/test_really_cool_feature.py -v
========================================= test session starts =====================
platform darwin -- Python 3.8.2, pytest-5.4.1, py-1.8.1, pluggy-0.13.1 --
cachedir: .pytest_cache
plugins: datadir-1.3.1
collected 11 items

ctdproc/tests/test_really_cool_feature.py::test_dtypes[int8] PASSED          [  9%]
ctdproc/tests/test_really_cool_feature.py::test_dtypes[int16] PASSED         [ 18%]
ctdproc/tests/test_really_cool_feature.py::test_dtypes[int32] PASSED         [ 27%]
ctdproc/tests/test_really_cool_feature.py::test_dtypes[int64] PASSED         [ 36%]
ctdproc/tests/test_really_cool_feature.py::test_mark[float32] PASSED         [ 45%]
ctdproc/tests/test_really_cool_feature.py::test_mark[int16] SKIPPED          [ 54%]
ctdproc/tests/test_really_cool_feature.py::test_mark[int32] XFAIL            [ 63%]
ctdproc/tests/test_really_cool_feature.py::test_series[int8] PASSED          [ 72%]
ctdproc/tests/test_really_cool_feature.py::test_series[int16] PASSED         [ 81%]
ctdproc/tests/test_really_cool_feature.py::test_series[int32] PASSED         [ 90%]
ctdproc/tests/test_really_cool_feature.py::test_series[int64] PASSED         [100%]

=============================== 9 passed, 1 skipped, 1 xfailed in 0.15s ===========

Tests that we have parametrized are now accessible via the test name, for example we could run these with -k int8 to sub-select only those tests which match int8.

(ctdproc) $ pytest ctdproc/tests/test_really_cool_feature.py -v -k int8
========================================= test session starts =====================
platform darwin -- Python 3.8.2, pytest-5.4.1, py-1.8.1, pluggy-0.13.1 --
plugins: datadir-1.3.1
collected 11 items / 9 deselected / 2 selected

ctdproc/tests/test_really_cool_feature.py::test_dtypes[int8] PASSED          [ 50%]
ctdproc/tests/test_really_cool_feature.py::test_series[int8] PASSED          [100%]

=================================== 2 passed, 9 deselected in 0.02s ===============

Running the test suite 

The tests can then be run directly inside your Git clone by typing:

pytest ctdproc

Often it is worth running only a subset of tests first around your changes before running the entire suite. The easiest way to do this is with:

pytest ctdproc/path/to/test.py -k regex_matching_test_name

Or with one of the following constructs:

pytest ctdproc/tests/[test-module].py
pytest ctdproc/tests/[test-module].py::[TestClass]
pytest ctdproc/tests/[test-module].py::[TestClass]::[test_method]

Documenting your code 

Changes should be reflected in the release notes located in HISTORY.rst. This file contains an ongoing change log for each release. Add an entry to this file to document your fix, enhancement or (unavoidable) breaking change. Make sure to include the GitHub issue number when adding your entry (using :issue:`1234`, where 1234 is the issue/pull request number).

If your code is an enhancement, it is most likely necessary to add usage examples to the existing documentation. This can be done following the section regarding documentation above.

Contributing your changes to ctdproc

Committing your code 

Keep style fixes to a separate commit to make your pull request more readable.

Once you’ve made changes, you can see them by typing:

git status

If you have created a new file, it is not being tracked by git. Add it by typing:

git add path/to/file-to-be-added.py

Doing ‘git status’ again should give something like:

# On branch shiny-new-feature
#
#       modified:   /relative/path/to/file-you-added.py
#

The following defines how a commit message should be structured:

A subject line with < 72 chars.

One blank line.

Optionally, a commit message body.

Please reference the relevant GitHub issues in your commit message using GH1234 or #1234. Either style is fine, but the former is generally preferred.

Now you can commit your changes in your local repository:

git commit -m

This will prompt you to type in your commit message.

Pushing your changes 

When you want your changes to appear publicly on your GitHub page, push your forked feature branch’s commits:

git push origin shiny-new-feature

Here origin is the default name given to your remote repository on GitHub. You can see the remote repositories:

git remote -v

If you added the upstream repository as described above you will see something like:

origin  git@github.com:yourname/ctdproc.git (fetch)
origin  git@github.com:yourname/ctdproc.git (push)
upstream        git://github.com/gunnarvoet/ctdproc.git (fetch)
upstream        git://github.com/gunnarvoet/ctdproc.git (push)

Now your code is on GitHub, but it is not yet a part of the ctdproc project. For that to happen, a pull request needs to be submitted on GitHub.

Review your code 

When you’re ready to ask for a code review, file a pull request. Before you do, once again make sure that you have followed all the guidelines outlined in this document regarding code style, tests, and documentation. You should also double check your branch changes against the branch it was based on:

Navigate to your repository on GitHub – https://github.com/your-user-name/ctdproc
Click on Branches
Click on the Compare button for your feature branch
Select the base and compare branches, if necessary. This will be main and shiny-new-feature, respectively.

Finally, make the pull request 

If everything looks good, you are ready to make a pull request. A pull request is how code from a local repository becomes available to the GitHub community and can be looked at and eventually merged into the main version. This pull request and its associated changes will eventually be committed to the main branch and available in the next release. To submit a pull request:

Navigate to your repository on GitHub
Click on the Pull Request button
You can then click on Commits and Files Changed to make sure everything looks okay one last time
Write a description of your changes in the Preview Discussion tab
Click Send Pull Request.

This request then goes to the repository maintainers, and they will review the code. If you need to make more changes, you can make them in your branch, add them to a new commit, push them to GitHub, and the pull request will be automatically updated. Pushing them to GitHub again is done by:

git push origin shiny-new-feature

This will automatically update your pull request with the latest code and restart the Continuous Integration tests.

Delete your merged branch (optional)

Once your feature branch is accepted into upstream, you’ll probably want to get rid of the branch. First, merge upstream main into your branch so git knows it is safe to delete your branch:

git fetch upstream
git checkout main
git merge upstream/main

Then you can do:

git branch -d shiny-new-feature

Make sure you use a lower-case -d, or else git won’t warn you if your feature branch has not actually been merged.

The branch will still exist on GitHub, so to delete it there do:

git push origin --delete shiny-new-feature

PR checklist 

Properly comment and document your code. See “Documenting your code”.
Test that the documentation builds correctly by typing make docs in the root directory or make html in the docs directory. This is not strictly necessary, but this may be easier than waiting for CI to catch a mistake. See “Contributing to the documentation”.
Test your code.
- Write new tests if needed. See “Test-driven development/code writing”.
- Test the code using Pytest. Running all tests (type pytest ctdproc or make test in the root directory) takes a while, so feel free to only run the tests you think are needed based on your PR (example: pytest ctdproc/tests/test_really_cool_feature.py). CI will catch any failing tests.
Properly format your code and verify that it passes the formatting guidelines set by Black and Flake8. See “Code formatting”. You can use pre-commit to run these automatically on each commit.
- Run black ctdproc in the root directory. This may modify some files. Confirm and commit any formatting changes.
- Run flake8 in the root directory. If this fails, it will log an error message.
- Alternatively, the above formatting steps are combined in make style. You can also type make style-check for a dry run.
Push your code and create a PR on GitHub.
Use a helpful title for your pull request by summarizing the main contributions rather than using the latest commit message. If this addresses an issue, please reference it.

Deploying 

A reminder for the maintainers on how to deploy. Make sure all your changes are committed (including an entry in HISTORY.rst). Then run:

$ bump2version patch # possible: major / minor / patch
$ git push
$ git push --tags

Travis will then deploy to PyPI if tests pass.