# Testing the code¶

All additions and modifications to ASE should be tested.

Test scripts should be put in the ase/test directory. Run all tests with:

ase test


This requires installing pytest, pytest-mock and pytest-xdist. You can install these dependencies automatically by running $$pip install ase[test]$$. See ase test --help for more information.

You can also run pytest directly from within the ase.test directory.

Important

When you fix a bug, add a test to the test suite checking that it is truly fixed. Bugs sometimes come back, do not give it a second chance!

## How to add a test¶

Create a module somewhere under ase.test. Make sure its name starts with test_. Inside the module, each test should be a function whose name starts with test_. This ensures that pytest finds the test. Use ase test --list to see which tests it will find.

You may note that many tests do not follow these rules. These are older tests. We expect to port them one day.

## How to fail successfully¶

The test suite provided by ase.test automatically runs all test scripts in the ase/test directory and summarizes the results.

If a test script causes an exception to be thrown, or otherwise terminates in an unexpected way, it will show up in this summary. This is the most effective way of raising awareness about emerging conflicts and bugs during the development cycle of the latest revision.

Remember, great tests should serve a dual purpose:

Working interface

To ensure that the class’es and method’s in ASE are functional and provide the expected interface. Empirically speaking, code which is not covered by a test script tends to stop working over time.

Replicable results

Even if a calculation makes it to the end without crashing, you can never be too sure that the numerical results are consistent. Don’t just assume they are, assert() it!

ase.test.assert(expression)

Raises an AssertionError if the expression does not evaluate to True.

Example:

from ase import molecule

def test_c60():
atoms = molecule('C60')
atoms.center(vacuum=4.0)
result = atoms.get_positions().mean(axis=0)
expected = 0.5*atoms.get_cell().diagonal()
tolerance = 1e-4
assert (abs(result - expected) < tolerance).all()


To run the same test with different inputs, use pytest fixtures. For example:

@pytest.mark.parametrize('parameter', [0.1, 0.3, 0.7])
def test_something(parameter):
# setup atoms here...
atoms.set_something(parameter)
# calculations here...
assert everything_is_going_to_be_alright