.. _contributing: ********************** Contributing to xarray ********************** .. note:: Large parts of this document came from the `Pandas Contributing Guide `_. Where to start? =============== All contributions, bug reports, bug fixes, documentation improvements, enhancements, and ideas are welcome. If you are brand new to *xarray* 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. Feel free to ask questions on the `mailing list `_. .. _contributing.bug_reports: Bug reports and enhancement requests ==================================== Bug reports are an important part of making *xarray* 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 out the bug-producing code on the *main* branch is often a worthwhile exercise to confirm that 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 xarray as xr ds = xr.Dataset(...) ... ``` #. Include the full version string of *xarray* and its dependencies. You can use the built in function:: ```python import xarray as xr xr.show_versions() ... ``` #. Explain why the current behavior is wrong/not desired and what you expect instead. The issue will then show up to the *xarray* community and be open to comments/ideas from others. .. _contributing.github: 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 *xarray* code base. .. _contributing.version_control: Version control, Git, and GitHub -------------------------------- To the new user, working with Git is one of the more daunting aspects of contributing to *xarray*. 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. .. _contributing.forking: Forking ------- You will need your own fork to work on the code. Go to the `xarray 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/xarray.git cd xarray git remote add upstream https://github.com/pydata/xarray.git This creates the directory `xarray` and connects your repository to the upstream (main project) *xarray* repository. .. _contributing.dev_env: Creating a development environment ---------------------------------- To test out code changes, you'll need to build *xarray* from source, which requires a Python environment. If you're making documentation changes, you can skip to :ref:`contributing.documentation` but you won't be able to build the documentation locally before pushing your changes. .. _contributiong.dev_python: Creating a Python Environment ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ Before starting any development, you'll need to create an isolated xarray development environment: - Install either `Anaconda `_ or `miniconda `_ - Make sure your conda is up to date (``conda update conda``) - Make sure that you have :ref:`cloned the repository ` - ``cd`` to the *xarray* source directory We'll now kick off a two-step process: 1. Install the build dependencies 2. Build and install xarray .. code-block:: sh # Create and activate the build environment conda create -c conda-forge -n xarray-tests python=3.8 # This is for Linux and MacOS conda env update -f ci/requirements/environment.yml # On windows, use environment-windows.yml instead conda env update -f ci/requirements/environment-windows.yml conda activate xarray-tests # or with older versions of Anaconda: source activate xarray-tests # Build and install xarray pip install -e . At this point you should be able to import *xarray* from your locally built version: .. code-block:: sh $ python # start an interpreter >>> import xarray >>> xarray.__version__ '0.10.0+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 before 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 *xarray*. 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 merge upstream/main This will combine your commits with the latest *xarray* 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, which can be reapplied after updating. .. _contributing.documentation: Contributing to the documentation ================================= If you're not the developer type, contributing to the documentation is still of huge value. You don't even have to be an expert on *xarray* to do so! In fact, there are sections of the docs that are worse off after being written by experts. If something in the docs doesn't make sense to you, updating the relevant section after you figure it out is a great way to ensure it will help the next person. .. contents:: Documentation: :local: About the *xarray* documentation -------------------------------- 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 *xarray* documentation consists of two parts: the docstrings in the code itself and the docs in this folder ``xarray/doc/``. 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. - The tutorials make heavy use of the `ipython directive `_ sphinx extension. This directive lets you put code in the documentation which will be run during the doc build. For example: .. code:: rst .. ipython:: python x = 2 x**3 will be rendered as:: In [1]: x = 2 In [2]: x**3 Out[2]: 8 Almost all code examples in the docs are run (and the output saved) during the doc build. This approach means that code examples will always be up to date, but it does make building the docs a bit more complex. - Our API documentation in ``doc/api.rst`` houses the auto-generated documentation from the docstrings. For classes, there are a few subtleties around controlling which methods and attributes have pages auto-generated. Every method should be included in a ``toctree`` in ``api.rst``, else Sphinx will emit a warning. How to build the *xarray* documentation --------------------------------------- Requirements ~~~~~~~~~~~~ Make sure to follow the instructions on :ref:`creating a development environment above `, but to build the docs you need to use the environment file ``ci/requirements/doc.yml``. .. code-block:: sh # Create and activate the docs environment conda env create -f ci/requirements/doc.yml conda activate xarray-docs # or with older versions of Anaconda: source activate xarray-docs # Build and install xarray pip install -e . Building the documentation ~~~~~~~~~~~~~~~~~~~~~~~~~~ To build the documentation run:: cd doc/ make html Then you can find the HTML output in the folder ``xarray/doc/_build/html/``. The first time you build the docs, it will take quite a while because it has to run all the code examples and build all the generated docstring pages. In subsequent evocations, Sphinx will try to only build the pages that have been modified. If you want to do a full clean build, do:: make clean make html .. _contributing.code: Contributing to the code base ============================= .. contents:: Code Base: :local: Code standards -------------- Writing good code is not just about what you write. It is also about *how* you write it. During :ref:`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 *xarray*. In addition, because a lot of people 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 ~~~~~~~~~~~~~~~ xarray uses several tools to ensure a consistent code format throughout the project: - `Black `_ for standardized code formatting - `blackdoc `_ for standardized code formatting in documentation - `Flake8 `_ for general code quality - `isort `_ for standardized order in imports. See also `flake8-isort `_. - `mypy `_ for static type checking on `type hints `_ We highly recommend that you setup `pre-commit hooks `_ to automatically run all the above tools every time you make a git commit. This can be done by running:: pre-commit install from the root of the xarray repository. You can skip the pre-commit checks with ``git commit --no-verify``. Backwards Compatibility ~~~~~~~~~~~~~~~~~~~~~~~ Please try to maintain backwards compatibility. *xarray* has a growing number of users with lots of existing code, so don't break it if at all possible. If you think breakage is required, clearly state why as part of the pull request. Be especially careful when changing function and method signatures, because any change may require a deprecation warning. For example, if your pull request means that the argument ``old_arg`` to ``func`` is no longer valid, instead of simply raising an error if a user passes ``old_arg``, we would instead catch it: .. code-block:: python def func(new_arg, old_arg=None): if old_arg is not None: from warnings import warn warn( "`old_arg` has been deprecated, and in the future will raise an error." "Please use `new_arg` from now on.", DeprecationWarning, ) # Still do what the user intended here This temporary check would then be removed in a subsequent version of xarray. This process of first warning users before actually breaking their code is known as a "deprecation cycle", and makes changes significantly easier to handle both for users of xarray, and for developers of other libraries that depend on xarray. .. _contributing.ci: Testing With Continuous Integration ----------------------------------- The *xarray* test suite runs automatically the `GitHub Actions `__, 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. This is an example of a green build. .. image:: _static/ci.png .. note:: Each time you push to your PR branch, a new run of the tests will be triggered on the CI. If they haven't already finished, tests for any older commits on the same branch will be automatically cancelled. .. _contributing.tdd: Test-driven development/code writing ------------------------------------ *xarray* 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. Adding tests is one of the most common requests after code is pushed to *xarray*. Therefore, it is worth getting in the habit of writing tests ahead of time so that this is never an issue. Like many packages, *xarray* 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 many current examples of tests, and we suggest looking to these for inspiration. If your test requires working with files or network connectivity, there is more information on the `testing page `_ of the wiki. The ``xarray.testing`` module has many special ``assert`` functions that make it easier to make statements about whether DataArray or Dataset objects are equivalent. 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:: def test_constructor_from_0d(): expected = Dataset({None: ([], 0)})[None] actual = DataArray(0) assert_identical(expected, actual) Transitioning to ``pytest`` ~~~~~~~~~~~~~~~~~~~~~~~~~~~ *xarray* existing test structure is *mostly* classed based, meaning that you will typically find tests wrapped in a class. .. code-block:: python class TestReallyCoolFeature: ... Going forward, we are moving to a more *functional* style using the `pytest `__ framework, which offers a richer testing framework that will facilitate testing and developing. Thus, instead of writing test classes, we will write test functions like this: .. code-block:: python def test_really_cool_feature(): ... Using ``pytest`` ~~~~~~~~~~~~~~~~ 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 - ``assert_equal`` and ``assert_identical`` from the ``xarray.testing`` module for xarray object comparisons. - the typical pattern of constructing an ``expected`` and comparing versus the ``result`` We would name this file ``test_cool_feature.py`` and put in an appropriate place in the ``xarray/tests/`` structure. .. TODO: confirm that this actually works .. code-block:: python import pytest import numpy as np import xarray as xr from xarray.testing import assert_equal @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 dataarray(): return xr.DataArray([1, 2, 3]) @pytest.fixture(params=["int8", "int16", "int32", "int64"]) def dtype(request): return request.param def test_series(dataarray, dtype): result = dataarray.astype(dtype) assert result.dtype == dtype expected = xr.DataArray(np.array([1, 2, 3], dtype=dtype)) assert_equal(result, expected) A test run of this yields .. code-block:: shell ((xarray) $ pytest test_cool_feature.py -v =============================== test session starts ================================ platform darwin -- Python 3.6.4, pytest-3.2.1, py-1.4.34, pluggy-0.4.0 -- cachedir: ../../.cache plugins: cov-2.5.1, hypothesis-3.23.0 collected 11 items test_cool_feature.py::test_dtypes[int8] PASSED test_cool_feature.py::test_dtypes[int16] PASSED test_cool_feature.py::test_dtypes[int32] PASSED test_cool_feature.py::test_dtypes[int64] PASSED test_cool_feature.py::test_mark[float32] PASSED test_cool_feature.py::test_mark[int16] SKIPPED test_cool_feature.py::test_mark[int32] xfail test_cool_feature.py::test_series[int8] PASSED test_cool_feature.py::test_series[int16] PASSED test_cool_feature.py::test_series[int32] PASSED test_cool_feature.py::test_series[int64] PASSED ================== 9 passed, 1 skipped, 1 xfailed in 1.83 seconds ================== 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``. .. code-block:: shell ((xarray) bash-3.2$ pytest test_cool_feature.py -v -k int8 =========================== test session starts =========================== platform darwin -- Python 3.6.2, pytest-3.2.1, py-1.4.31, pluggy-0.4.0 collected 11 items test_cool_feature.py::test_dtypes[int8] PASSED test_cool_feature.py::test_series[int8] PASSED Running the test suite ---------------------- The tests can then be run directly inside your Git clone (without having to install *xarray*) by typing:: pytest xarray The tests suite is exhaustive and takes a few minutes. 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 xarray/path/to/test.py -k regex_matching_test_name Or with one of the following constructs:: pytest xarray/tests/[test-module].py pytest xarray/tests/[test-module].py::[TestClass] pytest xarray/tests/[test-module].py::[TestClass]::[test_method] Using `pytest-xdist `_, one can speed up local testing on multicore machines, by running pytest with the optional -n argument:: pytest xarray -n 4 This can significantly reduce the time it takes to locally run tests before submitting a pull request. For more, see the `pytest `_ documentation. Running the performance test suite ---------------------------------- Performance matters and it is worth considering whether your code has introduced performance regressions. *xarray* is starting to write a suite of benchmarking tests using `asv `__ to enable easy monitoring of the performance of critical *xarray* operations. These benchmarks are all found in the ``xarray/asv_bench`` directory. asv supports both python2 and python3. To use all features of asv, you will need either ``conda`` or ``virtualenv``. For more details please check the `asv installation webpage `_. To install asv:: pip install git+https://github.com/spacetelescope/asv If you need to run a benchmark, change your directory to ``asv_bench/`` and run:: asv continuous -f 1.1 upstream/main HEAD You can replace ``HEAD`` with the name of the branch you are working on, and report benchmarks that changed by more than 10%. The command uses ``conda`` by default for creating the benchmark environments. If you want to use virtualenv instead, write:: asv continuous -f 1.1 -E virtualenv upstream/main HEAD The ``-E virtualenv`` option should be added to all ``asv`` commands that run benchmarks. The default value is defined in ``asv.conf.json``. Running the full benchmark suite can take up to one hour and use up a few GBs of RAM. Usually it is sufficient to paste only a subset of the results into the pull request to show that the committed changes do not cause unexpected performance regressions. You can run specific benchmarks using the ``-b`` flag, which takes a regular expression. For example, this will only run tests from a ``xarray/asv_bench/benchmarks/groupby.py`` file:: asv continuous -f 1.1 upstream/main HEAD -b ^groupby If you want to only run a specific group of tests from a file, you can do it using ``.`` as a separator. For example:: asv continuous -f 1.1 upstream/main HEAD -b groupby.GroupByMethods will only run the ``GroupByMethods`` benchmark defined in ``groupby.py``. You can also run the benchmark suite using the version of *xarray* already installed in your current Python environment. This can be useful if you do not have ``virtualenv`` or ``conda``, or are using the ``setup.py develop`` approach discussed above; for the in-place build you need to set ``PYTHONPATH``, e.g. ``PYTHONPATH="$PWD/.." asv [remaining arguments]``. You can run benchmarks using an existing Python environment by:: asv run -e -E existing or, to use a specific Python interpreter,:: asv run -e -E existing:python3.6 This will display stderr from the benchmarks, and use your local ``python`` that comes from your ``$PATH``. Information on how to write a benchmark and how to use asv can be found in the `asv documentation `_. .. TODO: uncomment once we have a working setup see https://github.com/pydata/xarray/pull/5066 The *xarray* benchmarking suite is run remotely and the results are available `here `_. Documenting your code --------------------- Changes should be reflected in the release notes located in ``doc/whats-new.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 :ref:`above `. Contributing your changes to *xarray* ===================================== 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 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/xarray.git (fetch) origin git@github.com:yourname/xarray.git (push) upstream git://github.com/pydata/xarray.git (fetch) upstream git://github.com/pydata/xarray.git (push) Now your code is on GitHub, but it is not yet a part of the *xarray* 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, performance 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/xarray #. 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 automatically be 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 :ref:`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, update your ``main`` branch to check that the merge was successful:: git fetch upstream git checkout main git merge upstream/main Then you can do:: git branch -D shiny-new-feature You need to use a upper-case ``-D`` because the branch was squashed into a single commit before merging. Be careful with this because ``git`` won't warn you if you accidentally delete an unmerged branch. If you didn't delete your branch using GitHub's interface, then it will still exist on GitHub. 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 html`` in the ``doc`` 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`` 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 xarray/tests/test_dataarray.py``). CI will catch any failing tests. - By default, the upstream dev CI is disabled on pull request and push events. You can override this behavior per commit by adding a [test-upstream] tag to the first line of the commit message. For documentation-only commits, you can skip the CI per commit by adding a "[skip-ci]" tag to the first line of the commit message. - **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 ``pre-commit run --all-files`` in the root directory. This may modify some files. Confirm and commit any formatting changes. - **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 the PR addresses an `issue `_, please `reference it `_.