Contributing to GC3Pie

First of all, thanks for wanting to contribute to GC3Pie! GC3Pie is an open-ended endeavour, and we’re always looking for new ideas, suggestions, and new code. (And also, for fixes to bugs old and new ;-))

The paragraphs below should brief you about the organization of the GC3Pie code repositories, and the suggested guidelines for code and documentation style. Feel free to request more info or discuss the existing recommendations on the GC3Pie mailing list

Code repository organization

GC3Pie code is hosted in a GitHub repository, which you can access online or using any Git client.

We encourage anyone to fork the repository and contribute back modifications in the form of pull requests.

The master branch should always be deployable: code in master should normally run without major known issues (but it may contain code that has yet not been released to PyPI). A tag is created on the master branch each time code is released to PyPI. Development happens on separate branches (or forks) which are then merged into master via pull requests.

Repository structure

The GC3Pie code repository has the following top-level structure; there is one subdirectory for each of the main parts of GC3Pie:

  • The gc3libs directory contains the GC3Libs code, which is the core of GC3Pie. GC3Libs are extensively described in the API section of this document; read the module descriptions to find out where your new suggested functionality would suit best. If unsure, ask on the GC3Pie mailing list.

  • The gc3utils directory contains the sources for the low-level GC3Utils command-line utilities.

  • The gc3apps directory contains the sources for higher level scripts that implement some computational use case of independent interest.

    The gc3apps directory contains one subdirectory per application script. Actually, each subdirectory can contain one or more Python scripts, as long as they form a coherent bundle; for instance, Rosetta is a suite of applications in computational biology: there are different GC3Apps script corresponding to different uses of the Rosetta suite, all of them grouped into the rosetta subdirectory.

    Subdirectories of the gc3apps directory follow this naming convention:

    • the directory name is the main application name, if the application that the scripts wrap is a known, publicly-released computational application (e.g., Rosetta, GAMESS)
    • the directory name is the requestor’s name, if the application that the scripts wrap is some research code that is being internally developed. For instance, the bf.uzh.ch directory contains scripts that wrap code for economic simulations that is being developed at the Banking and Finance Institute of the University of Zurich

Package generation

Due to issue 329, we don’t use the automatic discovery feature of setuptools, so the files included in the distributed packages are those in the MANIFEST.in file, please check The MANIFEST.in template section of the python documentation for a syntax reference. We usually include only code, documentation, and related files. We also include the regression tests, but we do not include the application tests in gc3apps/*/test directories.

Testing the code

In developing GC3Pie we try to use a Test Driven Development approach, in the light of the quote: It’s tested or it’s broken. We use tox and nose as test runners, which make creating tests very easy.

Running the tests

You can both run tests on your current environment using nosetests or use tox_ to create and run tests on separate environments. We suggest you to use nosetests while you are still fixing the problem, in order to be able to run only the failing test, but we strongly suggest you to run tox before committing your code.

Running tests with nosetests

In order to have the nosetests program, you need to install nose_ in your current environment and gc3pie must be installed in develop mode:

pip install nose
python setup.py develop

Then, from the top level directory, run the tests with:

nose -c nose.cfg

Nose will then crawl the directory tree looking for available tests. You can also specify a subset of the available sets, by:

  • specifying the directory from which nose should start looking for tests:

    # Run only backend-related tests
    nose -c nose.cfg gc3libs/backends
    
  • specifying the file containing the tests you want to run:

    # Run only tests contained in a specific file
    nose -c nose.cfg gc3libs/tests/test_session.py
    
  • specifying the id of the test (you need to run nose at least one to know which id is assigned to each test):

    # Run only test number 123
    nose -c nose.cfg 123
    

Running multiple tests

In order to test GC3Pie against multiple version of python we use tox, which creates virtual environments for all configured python version, runs nose inside each one of them, and prints a summary of the test results.

You don’t need to have tox installed in the virtual environment you use to develop gc3pie, you can create a new virtual environment and install tox on it with:

pip install tox

Running tox is straightforward; just type tox on the command-line in GC3Pie’s top level source directory.

The default tox.ini file shipped with GC3Pie attempts to test all Python versions from 2.4 to 2.7 (inclusive). If you want to run tests only for a specific version of python, for instance Python 2.6, use the -e option:

tox -e py26
[...]
Ran 118 tests in 14.168s

OK (SKIP=9)
__________________________________________________________ [tox summary] ___________________________________________________________
[TOX] py26: commands succeeded
[TOX] congratulations :)

(See section skipping tests for a discussion about how and when to define skipped tests.)

Option -r instructs tox to re-build the testing virtual environment. This is usually needed when you update the dependencies of GC3Pie or when you add or remove command line programs or configuration files. However, if you feel that the environments can be unclean, you can clean up everything by:

  1. deleting all the *.pyc file in your source tree:

    find . -name '*.pyc' -delete
    
  2. deleting and recreating tox virtual environments:

    tox -r
    

Organizing tests

Each single python file should have a test file inside a tests subpackage with filename created by prefixing test_ to the filename to test. For example, if you created a file foo.py, there should be a file tests/test_foo.py which will contains tests for foo.py.

Even though following the naming convention above is not always possible, each test regarding a specific component should be in a file inside a tests directory inside that component. For instance, tests for the subpackage gc3libs.persistence are located inside the directory gc3libs/persistence/tests but are not named after the specific file.

Writing tests

Please remember that it may be hard to understand, whenever a test fails, if it’s a bug in the code or in the tests! Therefore please remember:

  • Try to keep tests as simple as possible, and always simpler than the tested code. (Debugging is twice as hard as writing the code in the first place., Brian W. Kernighan and P. J. Plauger)
  • Write multiple indipendent tests to test different possible behavior and/or different methods of a class.
  • Tests should cover methods and functions, but also specific use cases.
  • If you are fixing a bug, it’s good practice to write a test to check if the bug is still there, in order to avoid to re-include the bug in the future.
  • Tests should clean up every temporary file they create.

Writing tests is very easy: just create a file whose name begins with test_, then put in it some functions which name begins with test_; the nose framework will automatically call each one of them. Moreover, nose will run also any doctest which will be found in the code.

Full documentation of the nose framework is available at the nose website. However, there are some of the interesting features you may want to use to improve your tests, detailed in the following sections.

Testing for errors

If your test must verify that the code raises an exception, instead of wrapping the test inside a try: ... except: block you can use the @raises decorator from the nose.tools module:

from nose.tools import raises

@raises(TypeError)
def test_invalid_invocation():
    Application()

This is exactly the same as writing:

try:
    Application()
    assert False, "we should have got an exception"
except TypeError:
    pass

Skipping tests

If you want to skip a test, just raise a SkipTest exception (imported from the nose.plugins.skip module). This is useful when you know that the test will fail, either because the code is not ready yet, or because some environmental conditions are not satisfied (e.g., an optional module is missing, or the code needs to access a service that is not available). For example:

from nose.plugins.skip import SkipTest
try:
    import MySQLdb
except ImportError:
    raise SkipTest("Error importing MySQL backend. Skipping MySQL low level tests")

Generating tests

It is possible to use Python generators to create multiple tests at run time:

def test_evens():
    for i in range(0, 5):
        yield check_even, i, i*3

def check_even(n, nn):
    assert n % 2 == 0 or nn % 2 == 0

This will result in five tests: nose will iterate the generator, creating a function test case wrapper for each tuple it yields. Specifically, in the example above, nose will execute the function calls check_even(0,0), check_even(1,3), ..., check_even(4,12) as if each of them were written in the source as a separate test; if any of them fails (i.e., raises an AssertionError), then the test is considered failed.

Grouping tests into classes

Tests that share the same set-up or clean-up code should be grouped into test classes:

  • The exact same set-up and clean-up code (fixtures) will be run before and after each test, but is written down only once.
  • Python class inheritance can be used to run the same tests on different configurations (e.g., by just overriding the set-up and clean-up code).

A test class is a regular Python class, whose name begins with Test (first letter must be uppercase); each method whose name begins with test_ defines a test case.

If the class defines a setUp method, it will be called before each test method. If the class defines a tearDown method, it will be called after each test method.

If class methods setup_class and teardown_class are defined, nose will invoke them once (before and after performing the tests of that class, respectively).

A canonical example of a test class with fixtures looks like this:

class TestClass(object):

   @classmethod
   def setup_class(cls):
      ...

   @classmethod
   def teardown_class(cls):
      ...

   def setUp(self):
      ...

   def tearDown(self):
      ...

   def test_case_1(self):
      ...

   def test_case_2(self):
      ...

   def test_case_3(self):
      ...

The nose framework will execute a code like this:

TestClass.setup_class()
for test_method in get_test_classes():
   obj = TestClass()
   obj.setUp()
   try:
      obj.test_method()
   finally:
      obj.tearDown()
TestClass.teardown_class()

That is, for each test case, a new instance of the TestClass is created, set up, and torn down – thus approximating the Platonic ideal of running each test in a completely new, pristine environment.

Opening the python debugger while running a test

When running using nosetests:command you cannot just execute pdb.set_trace() to open a debugger console. However, you can run the set_trace() function of the nose.tools module:

import nose.tools; nose.tools.set_trace()

Coding style

Python code should be written according to `PEP 8`_ recommendations. (And by this we mean not just the code style.)

Please take the time to read PEP 8 through, as it is widely-used across the Python programming community – it will benefit your contribution to any free/open-source Python project!

Anyway, here’s a short summary for the impatient:

  • use English nouns to name variables and classes; use verbs to name object methods.
  • use 4 spaces to indent code; never use TAB characters.
  • use lowercase letters for method and variable names; use underscores _ to separate words in multi-word identifiers (e.g., lower_case_with_underscores)
  • use “CamelCase” for class and exception names.
  • but, above all, do not blindly follow the rules and try to do the thing that enhances code clarity and readability!

Here’s other code conventions that apply to GC3Pie code; since they are not always widely followed or known, a short rationale is given for each of them.

  • Every class and function should have a docstring. Use reStructuredText markup for docstrings and documentation text files.

    Rationale: A concise English description of the purpose of a function can be faster to read than the code. Also, undocumented functions and classes do not appear in this documentation, which makes them invisible to new users.

  • Use fully-qualified names for all imported symbols; i.e., write import foo and then use foo.bar() instead of from foo import bar. If there are few imports from a module, and the imported names do clearly belong to another module, this rule can be relaxed if this enhances readability, but never do use unqualified names for exceptions.

    Rationale: There are so many functions and classes in GC3Pie, so it may be hard to know to which module the function count belongs. (Think especially of people who have to bugfix a module they didn’t write in the first place.)

  • When calling methods or functions that accept both positional and optional arguments like:

    def foo(a, b, key1=defvalue1, key2=defvalue2):
    

    always specify the argument name for optional arguments, which means do not call:

    foo(1, 2, value1, value2)
    

    but call instead:

    foo(1, 2, key1=value1, key2=value2)
    

    Rationale: calling the function with explicit argument names will reduce the risk of hit some compatibility issues. It is perfectly fine, from the point of view of the developer, to change the signature of a function by swapping two different optional arguments, so this change can happen any time, although changing positional arguments will break backward compatibility, and thus it’s usually well advertised and tested.

  • Use double quotes " to enclose strings representing messages meant for human consumption (e.g., log messages, or strings that will be printed on the users’ terminal screen).

    Rationale: The apostrophe character ' is a normal occurrence in English text; use of the double quotes minimizes the chances that you introduce a syntax error by terminating a string in its middle.

  • Follow normal typographic conventions when writing user messages and output; prefer clarity and avoid ambiguity, even if this makes the messages longer.

    Rationale: Messages meant to be read by users will be read by users; and if they are not read by users, they will be fired back verbatim on the mailing list on the next request for support. So they’d better be clear, or you’ll find yourself wondering what that message was intended to mean 6 months ago.

    Common typographical conventions enhance readability, and help users identify lines of readable text.

  • Use single quotes ' for strings that are meant for internal program usage (e.g., attribute names).

    Rationale: To distinguish them visually from messages to the user.

  • Use triple quotes """ for docstrings, even if they fit on a single line.

    Rationale: Visual distinction.

  • Each file should have this structure:

    • the first line is the hash-bang line,
    • the module docstring (explain briefly the module purpose and features),
    • the copyright and licence notice,
    • module imports (in the order suggested by PEP 8)
    • and then the code...

    Rationale: The docstring should be on top so it’s the first thing one reads when inspecting a file. The copyright notice is just a waste of space, but we’re required by law to have it.

Documentation

The documentation can be found in gc3pie/docs. It is generated using Sphinx (http://sphinx-doc.org/contents.html).

GC3Pie documentation is divided in three sections:

  • User Documentation: info on how to install, configure and run GC3Pie applications.
  • Programmer Documentation: info for programmers who want to use the GC3Pie libraries to write their own scripts and applications.
  • Contributors documentation: detailed information on how to contribute to GC3Pie and get your code included in the main library.

The GC3Libs programming API <gc3libs_> is the most relevant part of the docs for developers contributing code and is generated automatically from the docstrings inside the modules. Automatic documentation in Sphinx is described under http://sphinx-doc.org/tutorial.html#autodoc. While updating the docs of existing modules is simply done by running make html, adding documentation for a new module requires one of the following two procedures:

  • Add a reference to the new module in docs/programmers/api/index.rst. Additionally, create a file that enables automatic documentation for the module. For the module core.py, for example, automatic documentation is enabled by a file docs/programmers/api/gc3libs/core.rst with the following content:

    `gc3libs.core`
    ==============
    
    .. automodule:: gc3libs.core
         :members:
    
  • Execute the script docs/programmers/api/makehier.sh, which automates the above. Note that the makehier.sh script will re-create all .rst files for all GC3Pie modules, so check if there were some unexpected changes (e.g., with git status) before you commit!

Docstrings are written in reStructuredText format. To be able to cross-reference between differen objects in the documentation, you should be familiar with Sphinx domains in general and the Python domain in particular.

Questions?

Please write to the GC3Pie mailing list; we try to do our best to answer promptly.