parametrizing test classes in pytest
You can also apply parametrize
your class, so the same data will be sent to all test methods in the class.
First, create a list plasmas
that contains the plasma elements you want to pass to each test. Second, use the decorator @pytest.mark.parametrize
, and pass plasmas
to it.
plasmas = [plasma.LTEPlasma.from_abundance(t, {'Si':1.0}, 1e-13, atom_data, 10*86400) for t in range(2000, 20001, 1000)]
@pytest.mark.parametrize('plasma', plasmas)
class TestNormalLTEPlasma:
def test_beta_rad(self, plasma):
assert plasma.beta_rad == 1 / (10000 * constants.k_B.cgs.value)
def test_t_electron(self, plasma):
assert plasma.t_electron == 0.9 * plasma.t_rad
def test_saha_calculation_method(self, plasma):
assert plasma.calculate_saha == plasma.calculate_saha_lte
Instead of your setup function, create a parametrized test fixture:
ts = range(2000, 20001, 1000) # This creates a list of numbers from 2000 to 20000 in increments of 1000.
@pytest.fixture(params=ts)
def plasma(request):
return plasma.LTEPlasma.from_abundance(request.param, {'Si':1.0}, 1e-13, atom_data, 10*86400)
A "parametrized test fixture" is one where, when you use it in a test case, pytest will create a new test case for each parameter and run each separately.
You use the test fixture by adding a function argument called "plasma" to each of the test functions that want it:
class TestNormalLTEPlasma:
def test_beta_rad(self, plasma):
assert plasma.beta_rad == 1 / (10000 * constants.k_B.cgs.value)
def test_t_electron(self, plasma):
assert plasma.t_electron == 0.9 * plasma.t_rad
def test_saha_calculation_method(self, plasma):
assert plasma.calculate_saha == plasma.calculate_saha_lte
pytest takes care of collecting fixtures, collecting test functions, figuring out which test functions need which fixtures, and passing the fixture values to the test functions for execution.
Check out the docs for more details: https://docs.pytest.org/en/latest/fixture.html#fixture-parametrize