Development process

Here’s the long and short of it:

  1. If you are a first-time contributor:

    • Go to https://github.com/statlab/permute and click the “fork” button to create your own copy of the project.

    • Clone the project to your local computer:

      git clone git@github.com:your-username/permute.git

    • Add the upstream repository:

      git remote add upstream git@github.com:statlab/permute.git

    • Now, you have remote repositories named:

      • upstream, which refers to the permute repository

      • origin, which refers to your personal fork

  2. Develop your contribution:

    • Pull the latest changes from upstream:

      git checkout master
git pull upstream master

    • Create a branch for the feature you want to work on. Since the branch name will appear in the merge message, use a sensible name such as ‘permutation-speedups’:

      git checkout -b permutation-speedups

    • Commit locally as you progress (git add and git commit)

  3. To submit your contribution:

    • Push your changes back to your fork on GitHub:

      git push origin permuation-speedups

    • Go to GitHub. The new branch will show up with a green Pull Request button - click it.

    • If you want, post on the mailing list to explain your changes or to ask for review.

For a more detailed discussion, read these detailed documents on how to use Git with permute (http://statlab.github.io/permute/dev/gitwash/index.html).

  1. Review process:

    • Reviewers (the other developers and interested community members) will write inline and/or general comments on your Pull Request (PR) to help you improve its implementation, documentation and style. Every single developer working on the project has their code reviewed, and we’ve come to see it as friendly conversation from which we all learn and the overall code quality benefits. Therefore, please don’t let the review discourage you from contributing: its only aim is to improve the quality of project, not to criticize (we are, after all, very grateful for the time you’re donating!).

    • To update your pull request, make your changes on your local repository and commit. As soon as those changes are pushed up (to the same branch as before) the pull request will update automatically.

    • Continuous integration services are triggered after each Pull Request update to build the code, run unit tests, measure code coverage and check coding style (PEP8) of your branch. These tests must pass before your PR can be merged. If something fails, you can find out why by clicking on the “failed” icon (red cross) and inspecting the build and test log.

  2. Document changes

    Before merging your commits, you must add a description of your changes to the release notes of the upcoming version in doc/release/release_dev.rst.

Note

To reviewers: if it is not obvious, add a short explanation of what a branch did to the merge message and, if closing a bug, also add “Closes #123” where 123 is the issue number.

Divergence between upstream master and your feature branch

Do not ever merge the main branch into yours. If GitHub indicates that the branch of your Pull Request can no longer be merged automatically, rebase onto master:

git checkout master
git pull upstream master
git checkout permutation-speedups
git rebase master

If any conflicts occur, fix the according files and continue:

git add conflict-file1 conflict-file2
git rebase --continue

However, you should only rebase your own branches and must generally not rebase any branch which you collaborate on with someone else.

Finally, you must push your rebased branch:

git push --force origin permutation-speedups

(If you are curious, here’s a further discussion on the dangers of rebasing. Also see this LWN article.)

Guidelines

  • All code should have tests (see test coverage below for more details).

  • All code should be documented, to the same standard as NumPy and SciPy.

  • No changes are ever committed without review. Ask on the mailing list if you get no response to your pull request. Never merge your own pull request.

Stylistic Guidelines

  • Set up your editor to remove trailing whitespace. Follow PEP08. Check code with pyflakes / flake8.

  • Use numpy data types instead of strings (np.uint8 instead of "uint8").

  • Use the following import conventions:

    import numpy as np
import scipy as sp
import matplotlib as mpl
import matplotlib.pyplot as plt

cimport numpy as cnp # in Cython code

Commit message codes

Please prefix all commit summaries with one (or more) of the following labels. This should help others to easily classify the commits into meaningful categories:

  • BUG : bug fix

  • RFT : refactoring

  • ENH : new feature or extended functionality

  • BKW : addresses backward-compatibility

  • OPT : optimization

  • BRK : breaks something and/or tests fail

  • DOC: for all kinds of documentation related commits

  • TST : for adding or changing tests

  • DAT : for adding or changing data files

  • STY : PEP8 conformance, whitespace changes etc that do not affect function.

So your commit message might look something like this:

TST: relax test threshold slightly

Attempted fix for failure on windows test run when arrays are in fact
very close (within 6 dp).

Keeping up a habit of doing this is useful because it makes it much easier to see at a glance which changes are likely to be important when you are looking for sources of bugs, fixes, large refactorings or new features.

Pull request codes

When you submit a pull request to github, github will ask you for a summary. If your code is not ready to merge, but you want to get feedback, please consider using WIP - experimental optimization or similar for the title of your pull request. That way we will all know that it’s not yet ready to merge and that you may be interested in more fundamental comments about design.

When you think the pull request is ready to merge, change the title (using the Edit button) to something like MRG - optimization.

Test coverage

Tests for a module should ideally cover all code in that module, i.e., statement coverage should be at 100%.

To measure the test coverage, install coverage.py (e.g., using pip install coverage) and then run:

$ make coverage

This will print a report with one line for each file in permute, detailing the test coverage:

Name                 Stmts   Miss Branch BrMiss  Cover   Missing
----------------------------------------------------------------
permute                 43      6     10      1    87%   72, 77-88
permute.core            55      0     30      4    95%
permute.data            45      0      2      0   100%
permute.eda             22      0      8      0   100%
permute.irr             52      0     20      2    97%
permute.stratified      44      0     16      4    93%
----------------------------------------------------------------
TOTAL                  261      6     86     11    95%
----------------------------------------------------------------------
Ran 35 tests in 37.199s

OK

Bugs

Please report bugs on GitHub.

Working with np.random

If you need to set a random seed, do not use np.random.seed(). Using np.random.seed() can produce inconsistent results. Instead you should use get_prng() with your chosen seed. Here is a tiny example of how you might create a function that generates random numbers:

from permute.utils import get_prng

def func1(seed=None):
    prng = get_prng(seed)
    x = prng.randint(10)
    return x

If seed=None, then get_prng(seed) will try to read data from the system (e.g., /dev/urandom or the system clock) to initialize the pseudo-random number generator. See the docstring for more details.