Contributing#

Note

This contributing document is heavily based on pvlib-python contribution guidelines. This is still a work in progress

Encouraging more people to help develop bifacial_radiance is essential to our success. Therefore, we want to make it easy and rewarding for you to contribute.

There is a lot of material in this section, aimed at a variety of contributors from novice to expert. Don’t worry if you don’t (yet) understand parts of it.

Easy ways to contribute#

Here are a few ideas for how you can contribute, even if you are new to bifacial_radiance, git, or Python:

Ask and answer bifacial_radiance questions on StackOverflow and participate in discussions in the bifacial_radiance google group.
Make GitHub issues and contribute to the conversations about how to resolve them.
Read issues and pull requests that other people created and contribute to the conversation about how to resolve them.
Improve the documentation and the unit tests.
Improve the IPython/Jupyter Notebook tutorials or write new ones that demonstrate how to use bifacial_radiance in your area of expertise.
Tell your friends and colleagues about bifacial_radiance
Add your project to our Projects and publications that use bifacial_radiance wiki.

How to contribute new code#

The basics#

Contributors to bifacial_radiance use GitHub’s pull requests to add/modify its source code. The GitHub pull request process can be intimidating for new users, but you’ll find that it becomes straightforward once you use it a few times. Please let us know if you get stuck at any point in the process. Here’s an outline of the process:

Create a GitHub issue and get initial feedback from users and maintainers. If the issue is a bug report, please include the code needed to reproduce the problem.
Obtain the latest version of bifacial_radiance: Fork the bifacail_radiance project to your GitHub account, git clone your fork to your computer.
Make some or all of your changes/additions and git commit them to your local repository.
Share your changes with us via a pull request: git push your local changes to your GitHub fork, then go to GitHub make a pull request.

The Pandas project maintains an excellent contributing page that goes into detail on each of these steps. Also see GitHub’s Set Up Git and Using Pull Requests.

We strongly recommend using virtual environments for development. Virtual environments make it trivial to switch between different versions of software. This astropy guide is a good reference for virtual environments. If this is your first pull request, don’t worry about using a virtual environment.

You must include documentation and unit tests for any new or improved code. We can provide help and advice on this after you start the pull request. See the Testing section below.

Pull request scope#

This section can be summed up as “less is more”.

A pull request can quickly become unmanageable if too many lines are added or changed. “Too many” is hard to define, but as a rule of thumb, we encourage contributions that contain less than 50 lines of primary code. 50 lines of primary code will typically need at least 250 lines of documentation and testing. This is about the limit of what the maintainers can review on a regular basis.

A pull request can also quickly become unmanageable if it proposes changes to the API in order to implement another feature. Consider clearly and concisely documenting all proposed API changes before implementing any code.

Questions about related issues frequently come up in the process of addressing implementing code for a pull request. Please try to avoid expanding the scope of your pull request (this also applies to reviewers!). We’d rather see small, well-documented additions to the project’s technical debt than see a pull request languish because its scope expanded beyond what the reviewer community is capable of processing.

Of course, sometimes it is necessary to make a large pull request. We only ask that you take a few minutes to consider how to break it into smaller chunks before proceeding.

bifacial_radiance contains 3 layers of code: functions, analysis, and ModelChain. We recommend that contributors focus their work on only one or two of those layers in a single pull request.

When should I submit a pull request?#

The short answer: anytime.

The long answer: it depends. If in doubt, go ahead and submit. You do not need to make all of your changes before creating a pull request. Your pull requests will automatically be updated when you commit new changes and push them to GitHub.

There are pros and cons to submitting incomplete pull-requests. On the plus side, it gives everybody an easy way to comment on the code and can make the process more efficient. On the minus side, it’s easy for an incomplete pull request to grow into a multi-month saga that leaves everyone unhappy. If you submit an incomplete pull request, please be very clear about what you would like feedback on and what we should ignore. Alternatives to incomplete pull requests include creating a gist or experimental branch and linking to it in the corresponding issue.

The best way to ensure that a pull request will be reviewed and merged in a timely manner is to:

Start by creating an issue. The issue should be well-defined and actionable.
Ask the maintainers to tag the issue with the appropriate milestone.
Tag bifacial_radiance community members or @bifacial_radiance/maintainer when the pull request is ready for review. (see Pull request reviews)

Pull request reviews#

The bifacial_radiance community and maintainers will review your pull request in a timely fashion. Please “ping” @bifacial_radinace/maintainer if it seems that your pull request has been forgotten at any point in the pull request process.

Keep in mind that the PV modeling community is diverse and each bifacial_radiance community member brings a different perspective when reviewing code. Some reviewers bring years of expertise in the sub-field that your code contributes to and will focus on the details of the algorithm. Other reviewers will be more focused on integrating your code with the rest of bifacial_radiance, ensuring that it is feasible to maintain, that it meets the code style guidelines, and that it is comprehensively tested. Limiting the scope of the pull request makes it much more likely that all of these reviews can be conducted and any issues can be resolved in a timely fashion.

Sometimes it’s hard for reviewers to be immediately available, so the right amount of patience is to be expected. That said, interested reviewers should do their best to not wait until the last minute to put in their two cents.

Code style#

bifacial_radiance python generally follows the PEP 8 – Style Guide for Python Code. Maximum line length for code is 79 characters.

Code must be compatible with Python 3.5 and above.

bifacial_radiance uses a mix of full and abbreviated variable names. See Variables and Symbols. We could be better about consistency. Prefer full names for new contributions. This is especially important for the API. Abbreviations can be used within a function to improve the readability of formulae.

Set your editor to strip extra whitespace from line endings. This prevents the git commit history from becoming cluttered with whitespace changes.

Please see Documentation for information specific to documentation style.

Remove any logging calls and print statements that you added during development. warning is ok.

We typically use GitHub’s “squash and merge” feature to merge your pull request into bifacial_radiance. GitHub will condense the commit history of your branch into a single commit when merging into bifacial_radiance/master (the commit history on your branch remains unchanged). Therefore, you are free to make commits that are as big or small as you’d like while developing your pull request.

Documentation#

Documentation must be written in numpydoc format format which is rendered using the Sphinx Napoleon extension.

The numpydoc format includes a specification for the allowable input types. Python’s duck typing allows for multiple input types to work for many parameters. bifacial_radiance uses the following generic descriptors as short-hand to indicate which specific types may be used:

dict-like : dict, OrderedDict, pd.Series
numeric : scalar, np.array, pd.Series. Typically int or float dtype.
array-like : np.array, pd.Series. Typically int or float dtype.

Parameters that specify a specific type require that specific input type.

Read the Docs will automatically build the documentation for each pull request. Please confirm the documentation renders correctly by following the continuous-documentation/read-the-docs link within the checks status box at the bottom of the pull request.

To build the docs locally, install the doc dependencies specified in the setup.py file. See Installation instructions for more information.

Testing#

Developers must include comprehensive tests for any additions or modifications to bifacial_radiance. New unit test code should be placed in the corresponding test module in the bifacial_radiance/test directory.

A pull request will automatically run the tests for you on Linux platform and python versions 2.7 and 3.6. However, it is typically more efficient to run and debug the tests in your own local environment.

To run the tests locally, install the test dependencies specified in the setup.py file. See Installation instructions for more information.i

bifacial_radiance’s unit tests can easily be run by executing pytest on the bifacial_radiance directory:

pytest bifacial_radiance

or, for a single module:

pytest bifacial_radiance/test/modelchain.py

or, for a single test:

pytest bifacial_radiance/test/modelchain.py::runModelChain

We suggest using pytest’s --pdb flag to debug test failures rather than using print or logging calls. For example:

pytest bifacial_radiance/test/modelchain.py --pdb

will drop you into the pdb debugger at the location of a test failure. As described in Code style, bifacial_radiance code does not use print or logging calls, and this also applies to the test suite (with rare exceptions).

This documentation#

If this documentation is unclear, help us improve it! Consider looking at the pandas documentation for inspiration.

ModelChain

Variables and Symbols