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:
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
import BioSimSpace as BSS protocol = BSS.Protocol.Minimisation()
BioSimSpace is a collection of packages, e.g.
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,
- Where possible, external packages are hidden inside each module,
import mdtraj as _mdtraj
- Each module file contains an
__all__variable that lists the specific items that should be imported.
- The package
__init__.pycan be used to safely expose the required functionality to the user with:
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.
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.
# 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]
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]
First make sure that you are on the development branch of BioSimSpace:
git checkout devel
Now create and switch to a feature branch. This should be prefixed with feature, e.g.
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
PYTHONPATH=$HOME/Code/BioSimSpace/python $HOME/sire.app/bin/python script.py
or use the
develop argument when running the
setup.py script, i.e.
PYTHONPATH=$HOME/sire.app/bin/python setup.py develop
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.
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.:
# 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.
To run the full test suite, simply type:
(This assumes that you have made the
bin directory of your BioSimSpace or
Sire installation available to your
To run tests for a specific sub-module, e.g.:
To only run the unit tests in a particular file, e.g.:
To run a specific unit tests in a particular file, e.g.:
To get more detailed information about each test, run pytests using the verbose flag, e.g.:
More details regarding how to invoke
pytest can be found here.
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
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:
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:
assert 2 + 3 == approx(7, rel=2)
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.:
@pytest.mark.skip(reason="Not yet implemented.") def test_new_feature(): # A unit test for an, as yet, unimplemented feature. ...
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.:
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,
By marking the test in this manner it will be executed using all possible
(x, y), i.e.
(1, 3), (1, 4), (2, 3), (2, 4).
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.
Pytest provides a way of testing your code for known exceptions. For example,
suppose we had a function that raises an
def indexError(): """ A function that raises an IndexError. """ a =  a
We could then write a test to validate that the error is thrown as expected:
def test_indexError(): with pytest.raises(IndexError): indexError()
It’s possible to mark test functions with any attribute you like. For example:
@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.
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.
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.
$HOME/sire.app/bin/pip install sphinx sphinx_issues sphinx_rtd_theme
Then move to the
doc directory and run:
SPHINXBUILD=$HOME/sire.app/bin/sphinx-build make html
When finished, point your browser to
If you create new tests, please make sure that they pass locally before commiting. When happy, commit your changes, e.g.
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 to the BioSimSpace source code, or documentation,
e.g. fixing typos, then please add
***NO_CI*** to your commit message.
This will avoid unnecessarily running the
Azure pipelines, e.g.
building a new BioSimSpace binary, updating the website, etc.
Next, push your changes to the remote server, e.g.
# 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.