Basic Contribution Instructions

drizzlepac is an open source python package, and the source code is available in the DrizzlePac Github repository. New contributions and contributors are very welcome! Do not hesitate to reach out to the package maintainers if you are new to open-source development or if you have any questions/concerns. We just ask that all contributors adhere to the Space Telescope Code of Conduct.

Reporting bugs / requesting a new feature

If you would like to report a bug or request a new feature, this can be done by opening a new issue.

Contributing code

If you would like to contribute code, this is done by submitting a pull request to the “main” branch of spacetelescope/drizzlepac. To do this, we recommend the following workflow (which assumes you already have a Github account / command line tools). If you are also new to git, please refer to the git reference manual for an overview of git basics.

Step 1: Forking and cloning the drizzlepac repository

First, to clarify some terms that will be commonly used here:

• upstream refers to the main drizzlepac repository. this is where code from all contributors is ultimately merged into and where releases of the package will be made from.

• origin refers to the online fork you made of the upstream drizzlepac repository.

• local refers to the clone you made of the origin on your computer.

• The term remote in this context can refer to origin, or upstream. in general, it means anything hosted online on Github.

The first step is to create your own ‘remote’ (online) and ‘local’ (on your machine) clones of the central spacetelescope/drizzlepac repository. You will make code changes on your machine to your ‘local’ clone, push these to ‘origin’ (your online fork), and finally, open a pull request to the ‘’main’’ branch of spacetelescope/drizzlepac.

1. On the ‘spacetelescope/drizzlepac’ Github repository page, ‘fork’ the DrizzlePac repository to your own account space by clicking the appropriate button on the upper right-hand side. This will create an online clone of the main DrizzlePac repository but instead of being under the ‘spacetelescope’ organization, it will be under your personal account space. It is necessary to fork the repository so that many contributors aren’t making branches directly on ‘spacetelescope/drizzlepac’.

2. Now that you have remotely forked drizzlepac, it needs to be downloaded to your machine. To create this ‘local’ clone, choose an area on your file system and use the git clone command to dowload your remote fork on to your machine.

If you are using an SSH key to authenticate.

>> cd directory
>> git clone git@github.com:<your_username>/drizzlepac.git

Otherwise use the following.

>> cd directory
>> git clone https://github.com/<your_username>/drizzlepac.git

3. Make sure that your references to ‘origin’ and ‘upstream’ are set correctly - you will need this to keep everything in sync and push your changes online. While your inital local clone will be an exact copy of your remote, which is an exact copy of the ‘upstream’ spacetelescope/drizzlepac, these all must be kept in sync manually (via git fetch/pull/push).

   To check the current status of these references:

   >> git remote -v
   

After your inital clone, you will likely be missing the reference to ‘upstream’ (which is just the most commonly used name in git to refer to the main project repository - you can call this whatever you want but the origin/upstream conventions are most commonly used) - to set this, use the add git command:

If you are using an SSH key to authenticate.

>> git remote add upstream git@github.com:spacetelescope/drizzlepac.git

Otherwise, you can simply set it to the repository URL but you will have to enter your password every time you fetch/push

>> git remote add upstream https://github.com/spacetelescope/drizzlepac.git

If you ever want to reset these URLs, add references to other remote forks of drizzlepac for collaboration, or change from URL to SSH, you can use the related git remote set-url command.

Step 2: Creating a branch for your changes

It is a standard practice in git to create a new ‘branch’ (off upstream/main) for each new feature or bug fix. You can call this branch whatever you like - in this example, we’ll call it ‘my_feature’. First, make sure you have all recent changes to upstream by ‘fetching’ them:

>> git fetch upstream

The following will create a new branch off local/main called ‘my_feature’, and automatically switch you over to your new branch.

>> git checkout -b my_feature upstream/main

Step 3: Installing drizzlepac for development

Next, you will create a new Python environment where you will install your new branch of drizzlepac. We will show here how to use conda to manage environments, but there are other options.

1. Create a conda environment.

It is good practice to maintain different environments for different versions of DrizzlePac and its dependencies. You will likely want to maintain one, for example, for the latest released version of DrizzlePac (i.e. what you get by doing pip install drizzlepac), as well as one for development. Assuming the user has conda installed, here we will create a new conda environment called ‘drizzlepac_dev’ where we can install the new branch of our cloned repository.

>> conda create -n drizzlepac_dev python

Doing this will create a new environment with just some basic packages (i.e setuptools, pip) installed.

2. Installing drizzlepac in this environment

Make sure you are in your new environment:

>> conda activate drizzlepac_dev

And now in the top level of your local drizzlepac repository, ensuring you’re on the ‘my_feature’ branch:

>> pip install -e .

This will install drizzlepac from this cloned source code in ‘editable’ mode, meaning that you can import the code from this directory when within a Python session. This makes it easier for development because you can have the code you’re editing somewhere convenient in your file system vs. with other packages in ‘site-packages’. If you cloned the repository on your Desktop, for example, you can modify it there and Python will know that is where the source code is when you’re importing it within a Python session.

Note : If you use it, make sure to install iPython in your new environment as well. Otherwise, it will pick up packages from the base environment instead.

Step 4: Making code changes

Now that you’ve forked, cloned, made a new branch for your feature, and installed it in a new environment for development of drizzlepac, you are ready to make changes to the code. As you make changes, make sure to git commit -m <"some message"> frequently (in case you need to undo something by reverting back to a previous commit - you cant do this if you commit everything at once!). After you’ve made your desired changes, and committed these changes, you will need to push them online to your ‘remote’ fork of drizzlepac:

>> git push origin my_feature

If the changes are significant, please make an entry in CHANGELOG.rst in the top level drizzlepac directory with a short description of the changes you’ve made and, once you open a pull request, add the corresponding PR number.

Step 4: Opening a pull request

Now, you can open a pull request on the main branch of the upstream drizzlepac repository.

1. On the spacetelescope/drizzlepac web page, after you push your changes you should see a large green banner appear at the top prompting you to open a pull request with your recently pushed changes. You can also open a pull request from the pull request tab on that page. Select your fork and your ‘my_feature’ branch, and open a pull request against the ‘main’ branch.

2. There is now a checklist of items that need to be done before your PR can be merged.

   • The continuous integration (CI) tests must complete and pass. The CI runs several different checks including running the unit tests, ensuring the documentation builds, checking for code style issues (see the PEP8 style guide), and ensuring any changes are covered by unit tests. The CI runs upon opening a PR, and will re-run any time you push commits to that branch.

   • You will need to add a change log entry in CHANGES.rst if your contribution is a new feature or bug fix. An entry is not required for small fixes like typos.

   • Your PR will need to be reviewed and approved by at least one maintainers. They may require changes from you before your code can be merged, in which case you will need to go back and make these changes and push them (they will automatically appear in the PR when they’re pushed to origin/my_feature).

Advanced Contribution Instructions

Keeping your development branch current - rebasing

As drizzlepac is constantly evolving, you will often encounter the situation where you’ve made changes to your branch off ‘main’, but in the time its taken you to make those changes, ‘upstream/main’ has evolved with new commits from other developers. In this situation, you will want to make sure you incorporate these changes into your branch. Rebasing allows you to do two things - 1. apply others changes on top of yours, and 2. squash your commits, even if there aren’t new changes to apply.

Periodically, while writing code, to keep your branch up to date you will want to do an interactive rebase against upstream/main to apply any new changes on top of yours:

>> git rebase -i upstream/main

This will then prompt you to select commits and commit messages - if you select just the top commit, this will ‘squash’ the others and combine them into one. You will be prompted to resolve any conflicts if you’ve made modifications to the same file someone else has. Once you’ve completed your rebase, you must force push your branch to origin/my_feature to make your local and remote match.

>> git push -f origin/my_feature

Before merging a PR, we typically would like you to rebase and squash all of your commits into a single one, which is also done with git rebase

Writing and building documentation

drizzlepac uses sphinx to generate documentation, which is then hosted online on readthedocs.

You can access the documentation on the DrizzlePac readthedocs website - the ‘latest’ version is whatever is currently on the main branch. If you successfully merge a PR with documentation changes, they will only appear on ‘latest’ until the next DrizzlePac release.

All of the documentation resides in the drizzlepac/docs subdirectories (mainly within directories in drizzlepac/docs/drizzlepac, organized by module). The documentation is written in .rst (reStructured text) files - if you wish to make changes or add to the documentation, do so in these files in the appropriate location. reStructured text is the markup language used by sphinx, for information on the syntax refer to the sphinx documentation.

While writing documentation, you will want to make sure the documentation builds successfully (i.e, produces a working .html file). This happens on the CI when you open a pull request, but it is a good idea to build the documentation yourself locally as well. Before you do this, you will need to make sure you have the correct dependencies installed in your current environment. All of these optional dependencies are specified in pyproject.toml and include things like the correct version of sphinx, as well as the necessary sphinx themes that the project uses. These do not install automatically when you install drizzlepac unless directly specified. To do this, while in the top level directory of drizzlepac on your my_feature branch:

>> pip install -e ".[docs]"

Now, with the correct documentation dependencies installed, you can attempt to build the documentation locally. To do this, enter into the drizzlepac/docs subdirectory and do:

>> make html

If the documentation successfully builds, the output HTML files will be output in the _build/html subdirectory. You can open the main index.html file in your browser and explore the full documentation pages just like the readthedocs site. If there were any errors or warnings, a traceback will be printed on your terminal. Small errors, like a typo in a link to another section, can cause frustrating errors so it is good practice to build the docs frequently when editing them.

Writing and running unit tests

Unit tests are located in the tests directory and are separated by instrument, with additional separate directories for Hubble Advanced Products (HAP) and the drizzle algorithm. These tests run the code on simplified datasets to make sure there are no breaking changes introduced. We aim to cover most lines of drizzlepac with unit test, so when adding code you will often need to write a new test or add to an existing test to ensure adequate coverage.

Take a look around at the existing tests - many tests use a @pytest.fixture to set up a common dataset. The test functions themselves set up a scenario to run a function under certain conditions, and culminate in a set of assertions that need to pass (or fail if the test is marked as xfail).

The CI will run the unit tests on your branch when you open a pull request. They will re-run every time you push commits to this remote branch as well. Unit tests must pass on any changes you make, and if you’re introducing new code that isn’t covered by an existing test, you will need to write one. The codecoverage check that runs on the CI when you open a pull request will tell you if you’ve introduced any new lines that aren’t covered by a test.

You can also run unit tests locally, and you should do this periodically to test your code. To do this, you will need to install the optional dependencies needed for running tests.

>> pip install -e ".[test]"

This will install the optional ‘test’ dependencies specified in pyproject.toml that don’t install by default. The package pytest is one of these and is what’s used to run the tests. pytest searches through all the directories in your repository (underneath the directory from which it was invoked command line) and looks for any directories called ‘test’ or .py files with the word ‘test’ in the name. Functions in these files will be executed.

To run all of the drizzlepac unit tests, while in the drizzlepac/ level directory, simply run the command:

>> pytest

If you want to run all the tests for a single instrument/directory, for example WFC3, you can run this from ‘drizzlepac/tests/wfc3’.

To run all tests within a single test file (for example, all tests in test_wfc3).

>> pytest drizzlepac/tests/wfc3/test_wfc3.py

Configuring pytest for unit tests

pytest can be configured with many different flags - see the pytest documentation to see all of these. Here we will summarize a few of the most useful options.

If you are writing and debugging a test, you may find it helpful to have print statements printed to the terminal while running tests (which doesn’t happen by default). To do this,

>> pytest -s

If you want to only run a specific test function within a file (or only tests with names that contain a certain string):

>> pytest -k testname.

This will search all files under the directory you’re in for files or functions with the string ‘test’ in them, and within those files run only the test functions that contain the string testname.

Within the test files themselves, decorators can be used to control the behavior of the test. Some of the more useful decorators include: 1. @pytest.mark.parametrize can be used to run a single test on multiples sets of input parameters 2. @pytest.skip can be used to skip tests altogether, or under specific conditions (for example, only when being run by the CI) 3. @pytest.fixture to declare a fixture 1. @pytest.mark.xfail will make a test pass only if it fails.

STScI Regression Tests

In addition to unit tests, drizzlepac has a set of regression tests. Many of tests must be run on the STScI system and are not available to the public. These internal tests can be run using the following command, and as the names suggest, require storage space (~8 GB) and time (~3-4 hours) to run.

>> pytest --bigdata --slow

Running the pytests without these options will result in a number of tests being skipped and labeled as expected failures (xfails).

Simultaneously developing drizzlepac and one of its dependencies

If you encounter the scenario where you wish to simultaneously make changes in drizzlepac and also in one of its dependencies like fitsblender or stsci.tools, we recommend that you create a new environment with development versions of both of these packages. To do this, you can follow the same workflow outlined in the ‘Contributing code’ section of this guide. To summarize, you will want to create a new Python environment (called, for example, drizzlepac_fitsblender_dev), fork and clone a local copy of drizzlepac if you haven’t already and install this (doing pip install -e . in the top level directory of drizzlepac), and then fork and clone fitsblender and install it in this environment in the same way. To double check that you have the correct dev versions of these packages in your environment, you can check their versions by doing:

>> conda list

When opening up two dependent pull requests in drizzlepac and one of its dependency packages, unit tests will not pass on the CI because the pyproject.toml file in drizzlepac points to the last released version of fitsblender, and fitsblender points to the last version of drizzlepac, so the issue becomes circular. What you will need to do is modify the pyproject.toml files in both packages to point to the other to demonstrate that CI tests pass (and make a comment noting this in your PR), and then change it back before the PR is merge so that changes to pyproject.toml are not merged into main. In your drizzlepac branch, to point to your branch in the dependent package (in this example fitsblender), change the required fitsblender version in pyproject.toml to:

>> fitsblender @  git+https://github.com/<your_username>/fitsblender.git@<your_branch>

And similarly, in fitsblender, change the required drizzlepac version to:

>> drizzlepac @  git+https://github.com/<your_username>/drizzlepac.git@<your_branch>

Let the CI run and ensure it passes, comment this in your PR and make sure the reviewers confirm, and then change the versions back before your PR is merged (which will again cause the CI to fail, but that’s OK).