================= Developer's guide ================= .. toctree:: :maxdepth: 2 The source code for BioSimSpace is available on `GitHub `__. Python ====== BioSimSpace uses the Python programming language. Our aim is to provide a simple and robust API where unnecessary implementation details are hidden from the user. "\ *Hold on a second, this code isn't very Pythonic!*\ " Indeed it is not, but this is a design choice. BioSimSpace is intended primarily to be used by novices, who may be unfamiliar with Python, or programming in general. We want to make it as easy as possible for these users to get up and running with molecular simulation. BioSimSpace also needs to be robust and portable, hence we need to use encapsulation to shield the user from unintended consequences. With this in mind, we use the following coding conventions: Naming ------ We follow a C++ style naming convention. * Packages: CamelCase * Classes: CamelCase * Methods: camelCase * Functions: camelCase * Variables: snake_case For example, to instantiate a minimisation protocol from the ``Protocol`` package: .. code-block:: python import BioSimSpace as BSS protocol = BSS.Protocol.Minimisation() Modules ------- BioSimSpace is a collection of packages, e.g. ``BioSimSpace.Gateway`` and ``BioSimSpace.Protocol``. Within each package is a set of modules that implement the required functionality. Rather than directly exposing all of the modules we choose to hide implementation details from the user. Instead we use the package ``__init__.py`` to selectively import the required classes and functions. * Module files containing implementation details are prefixed with an underscore, i.e. ``_process.py`` * Where possible, external packages are hidden inside each module, e.g. ``import mdtraj as _mdtraj`` * Each module file contains an ``__all__`` variable that lists the specific items that should be imported. * The package ``__init__.py`` can be used to safely expose the required functionality to the user with: .. code-block:: python from module import * This results in a clean API and documentation, with all extraneous information, e.g. external modules, hidden from the user. This is important when working interactively, since `IPython `__ and `Jupyter `__ do not respect the ``__all__`` variable when auto-completing, meaning that the user will see a full list of the available names when hitting tab. When following the conventions above, the user will only be able to access the exposed names. This greatly improves the clarity of the package, allowing a new user to quickly determine the available functionality. Any user wishing expose further implementation detail can, of course, type an underscore to show the hidden names when searching. Encapsulation ------------- BioSimSpace aims to provide a means of writing robust and portable workflow components (nodes). To this end, we choose to use an object oriented approach where data is encapsulated, with getters used to retrieve data from an object. To avoid unintended consequences, getters that return mutable data types, e.g. lists and dictionaries, should return a copy of the data. This prevents the user unintentionally modifying the private data contained in the object. Setters should be used to explicitly modify member data. For example: .. code-block:: python # A class that holds a list of numbers. class MyClass(): # A private class member variable containing a list of numbers. _list = [1, 2, 3, 4, 5] def getList(self): return self._list # Create an instance of the class. c = MyClass() n = c.getList() print(n) [1, 2, 3, 4, 5] # Update n. n.append(6) # The private member data has been modified! print(c.getList()) [1, 2, 3, 4, 5, 6] Instead use: .. code-block:: python class MyClass(): # A private class member variable containing a list of numbers. _list = [1, 2, 3, 4, 5] def getList(self): return self._list.copy() # Create an instance of the class. c = MyClass() n = c.getList() print(n) [1, 2, 3, 4, 5] # Update n. n.append(6) # The private member data is untouched. print(c.getList()) [1, 2, 3, 4, 5] Workflow ======== Feature branches ---------------- First make sure that you are on the development branch of BioSimSpace: .. code-block:: bash git checkout devel Now create and switch to a feature branch. This should be prefixed with *feature*, e.g. .. code-block:: bash git checkout -b feature-process While working on your feature branch you won't want to continually re-install in order to make the changes active. To avoid this, you can either make use of ``PYTHONPATH``, e.g. .. code-block:: bash PYTHONPATH=$HOME/Code/BioSimSpace/python python script.py or use the ``develop`` argument when running the ``setup.py`` script, i.e. .. code-block:: bash python setup.py develop Testing ------- When working on your feature it is important to write tests to ensure that it does what is expected and doesn't break any existing functionality. Tests should be placed inside the ``test`` directory, creating an appropriately named sub-directory for any new packages. The test suite is intended to be run using `pytest `__. When run, ``pytest`` searches for tests in all directories and files below the current directory, collects the tests together, then runs them. Pytest uses name matching to locate the tests. Valid names start or end with *test*\ , e.g.: .. code-block:: python # Files: test_file.py file_test.py # Functions: def test_func(): def func_test(): We use the convention of ``test_*`` when naming files and functions. Running tests ^^^^^^^^^^^^^ To run the full test suite, simply type: .. code-block:: bash pytest (This assumes that you have made the ``bin`` directory of your BioSimSpace or Sire installation available to your ``PATH``.) To run tests for a specific sub-module, e.g.: .. code-block:: bash pytest test/Process To only run the unit tests in a particular file, e.g.: .. code-block:: bash pytest test/Process/test_namd.py To run a specific unit tests in a particular file, e.g.: .. code-block:: bash pytest test/Process/test_namd.py::test_minimise To get more detailed information about each test, run pytests using the *verbose* flag, e.g.: .. code-block:: bash pytest -v More details regarding how to invoke ``pytest`` can be found `here `__. Writing tests ^^^^^^^^^^^^^ Basics """""" Try to keep individual unit tests short and clear. Aim to test one thing, and test it well. Where possible, try to minimise the use of ``assert`` statements within a unit test. Since the test will return on the first failed assertion, additional contextual information may be lost. Floating point comparisons """""""""""""""""""""""""" Make use of the `approx `__ function from the ``pytest`` package for performing floating point comparisons, e.g: .. code-block:: python from pytest import approx assert 0.1 + 0.2 == approx(0.3) By default, the ``approx`` function compares the result using a relative tolerance of 1e-6. This can be changed by passing a keyword argument to the function, e.g: .. code-block:: python assert 2 + 3 == approx(7, rel=2) Skipping tests """""""""""""" If you are using `test-driven development `__ it might be desirable to write your tests before implementing the functionality, i.e. you are asserting what the *output* of a function should be, not how it should be *implemented*. In this case, you can make use of the ``pytest`` *skip* decorator to flag that a unit test should be skipped, e.g.: .. code-block:: python @pytest.mark.skip(reason="Not yet implemented.") def test_new_feature(): # A unit test for an, as yet, unimplemented feature. ... Parametrizing tests """"""""""""""""""" Often it is desirable to run a test for a range of different input parameters. This can be achieved using the ``parametrize`` decorator, e.g.: .. code-block:: python import pytest from operator import mul @pytest.mark.parametrize("x", [1, 2]) @pytest.mark.parametrize("y", [3, 4]) def test_mul(x, y): """ Test the mul function. """ assert mul(x, y) == mul(y, x) Here the function test_mul is parametrized with two parameters, ``x`` and ``y``. By marking the test in this manner it will be executed using all possible parameter pairs ``(x, y)``\ , i.e. ``(1, 3), (1, 4), (2, 3), (2, 4)``. Alternatively: .. code-block:: python import pytest from operator import sub @pytest.mark.parametrize("x, y, expected", [(1, 2, -1), (7, 3, 4), (21, 58, -37)]) def test_sub(x, y, expected): """ Test the sub function. """ assert sub(x, y) == -sub(y, x) == expected Here we are passing a list containing different parameter sets, with the names of the parameters matched against the arguments of the test function. Testing exceptions """""""""""""""""" Pytest provides a way of testing your code for known exceptions. For example, suppose we had a function that raises an ``IndexError``\ : .. code-block:: python def indexError(): """ A function that raises an IndexError. """ a = [] a[3] We could then write a test to validate that the error is thrown as expected: .. code-block:: python def test_indexError(): with pytest.raises(IndexError): indexError() Custom attributes """"""""""""""""" It's possible to mark test functions with any attribute you like. For example: .. code-block:: python @pytest.mark.slow def test_slow_function(): """ A unit test that takes a really long time. """ ... Here we have marked the test function with the attribute ``slow`` in order to indicate that it takes a while to run. From the command line it is possible to run or skip tests with a particular mark. .. code-block:: python pytest mypkg -m "slow" # only run the slow tests pytest mypkg -m "not slow" # skip the slow tests The custom attribute can just be a label, as in this case, or could be your own function decorator. Documentation ------------- BioSimSpace is fully documented using `NumPy `__ style docstrings. See `here `__ for details. The documentation is automatically built using `Sphinx `__ whenever a commit is pushed to devel, which will then update this website. To build the documentation locally you will first need to install some additional packages. .. code-block:: bash pip install sphinx sphinx_issues sphinx_rtd_theme Then move to the ``doc`` directory and run: .. code-block:: bash sphinx-build make html When finished, point your browser to ``build/html/index.html``. Committing ---------- If you create new tests, please make sure that they pass locally before committing. When happy, commit your changes, e.g. .. code-block:: bash git commit python/BioSimSpace/Feature/new_feature.py test/Feature/test_feature \ -m "Implementation and test for new feature." Remember that it is better to make small changes and commit frequently. If your edits don't change the BioSimSpace source code, or documentation, e.g. fixing typos, then please add ``ci skip`` to your commit message. This will avoid unnecessarily triggering `GitHub actions `__, e.g. building a new BioSimSpace binary, updating the website, etc. To this end, we have provided a git hook that will append ``ci skip`` if the commit only modifies files in a blacklist that is specified in the file ``.ciignore`` (analogous to the ``.gitignore`` used to ignore untracked files). To enable the hook, simply copy it into the ``.git/hooks`` directory: .. code-block:: bash cp git_hooks/commit-msg .git/hooks Any additional files or paths that shouldn't trigger a re-build can be added to the ``.ciignore`` file. Next, push your changes to the remote server, e.g. .. code-block:: bash # Push to the feature branch on the main BioSimSpace repo, if you have access. git push origin feature # Push to the feature branch your own fork. git push fork feature When the feature is complete, create a *pull request* on GitHub so that the changes can be merged back into the development branch. For information, see the documentation `here `__.