.. _paramexamples: Parametrizing tests ================================================= .. currentmodule:: _pytest.python ``pytest`` allows to easily parametrize test functions. For basic docs, see :ref:`parametrize-basics`. In the following we provide some examples using the builtin mechanisms. Generating parameters combinations, depending on command line ---------------------------------------------------------------------------- .. regendoc:wipe Let's say we want to execute a test with different computation parameters and the parameter range shall be determined by a command line argument. Let's first write a simple (do-nothing) computation test:: # content of test_compute.py def test_compute(param1): assert param1 < 4 Now we add a test configuration like this:: # content of conftest.py def pytest_addoption(parser): parser.addoption("--all", action="store_true", help="run all combinations") def pytest_generate_tests(metafunc): if 'param1' in metafunc.fixturenames: if metafunc.config.option.all: end = 5 else: end = 2 metafunc.parametrize("param1", range(end)) This means that we only run 2 tests if we do not pass ``--all``:: $ py.test -q test_compute.py .. 2 passed in 0.12 seconds We run only two computations, so we see two dots. let's run the full monty:: $ py.test -q --all ....F ======= FAILURES ======== _______ test_compute[4] ________ param1 = 4 def test_compute(param1): > assert param1 < 4 E assert 4 < 4 test_compute.py:3: AssertionError 1 failed, 4 passed in 0.12 seconds As expected when running the full range of ``param1`` values we'll get an error on the last one. Different options for test IDs ------------------------------------ pytest will build a string that is the test ID for each set of values in a parametrized test. These IDs can be used with ``-k`` to select specific cases to run, and they will also identify the specific case when one is failing. Running pytest with ``--collect-only`` will show the generated IDs. Numbers, strings, booleans and None will have their usual string representation used in the test ID. For other objects, pytest will make a string based on the argument name:: # content of test_time.py from datetime import datetime, timedelta testdata = [(datetime(2001, 12, 12), datetime(2001, 12, 11), timedelta(1)), (datetime(2001, 12, 11), datetime(2001, 12, 12), timedelta(-1)), ] @pytest.mark.parametrize("a,b,expected", testdata) def test_timedistance_v0(a, b, expected): diff = a - b assert diff == expected @pytest.mark.parametrize("a,b,expected", testdata, ids=["forward", "backward"]) def test_timedistance_v1(a, b, expected): diff = a - b assert diff == expected def idfn(val): if isinstance(val, (datetime,)): # note this wouldn't show any hours/minutes/seconds return val.strftime('%Y%m%d') @pytest.mark.parametrize("a,b,expected", testdata, ids=idfn) def test_timedistance_v2(a, b, expected): diff = a - b assert diff == expected In ``test_timedistance_v0``, we let pytest generate the test IDs. In ``test_timedistance_v1``, we specified ``ids`` as a list of strings which were used as the test IDs. These are succinct, but can be a pain to maintain. In ``test_timedistance_v2``, we specified ``ids`` as a function that can generate a string representation to make part of the test ID. So our ``datetime`` values use the label generated by ``idfn``, but because we didn't generate a label for ``timedelta`` objects, they are still using the default pytest representation:: $ py.test test_time.py --collect-only ======= test session starts ======== platform linux2 -- Python 2.7.10, pytest-2.8.1.dev1, py-1.4.30, pluggy-0.3.1 rootdir: $REGENDOC_TMPDIR, inifile: collected 0 items / 1 errors ======= ERRORS ======== _______ ERROR collecting test_time.py ________ $PYTHON_PREFIX/lib/python2.7/site-packages/_pytest/python.py:581: in _importtestmodule mod = self.fspath.pyimport(ensuresyspath=importmode) $PYTHON_PREFIX/lib/python2.7/site-packages/py/_path/local.py:650: in pyimport __import__(modname) E File "$REGENDOC_TMPDIR/test_time.py", line 6 E E ^ E SyntaxError: invalid syntax ======= 1 error in 0.12 seconds ======== A quick port of "testscenarios" ------------------------------------ .. _`test scenarios`: http://pypi.python.org/pypi/testscenarios/ Here is a quick port to run tests configured with `test scenarios`_, an add-on from Robert Collins for the standard unittest framework. We only have to work a bit to construct the correct arguments for pytest's :py:func:`Metafunc.parametrize`:: # content of test_scenarios.py def pytest_generate_tests(metafunc): idlist = [] argvalues = [] for scenario in metafunc.cls.scenarios: idlist.append(scenario[0]) items = scenario[1].items() argnames = [x[0] for x in items] argvalues.append(([x[1] for x in items])) metafunc.parametrize(argnames, argvalues, ids=idlist, scope="class") scenario1 = ('basic', {'attribute': 'value'}) scenario2 = ('advanced', {'attribute': 'value2'}) class TestSampleWithScenarios: scenarios = [scenario1, scenario2] def test_demo1(self, attribute): assert isinstance(attribute, str) def test_demo2(self, attribute): assert isinstance(attribute, str) this is a fully self-contained example which you can run with:: $ py.test test_scenarios.py ======= test session starts ======== platform linux2 -- Python 2.7.10, pytest-2.8.1.dev1, py-1.4.30, pluggy-0.3.1 rootdir: $REGENDOC_TMPDIR, inifile: collected 4 items test_scenarios.py .... ======= 4 passed in 0.12 seconds ======== If you just collect tests you'll also nicely see 'advanced' and 'basic' as variants for the test function:: $ py.test --collect-only test_scenarios.py ======= test session starts ======== platform linux2 -- Python 2.7.10, pytest-2.8.1.dev1, py-1.4.30, pluggy-0.3.1 rootdir: $REGENDOC_TMPDIR, inifile: collected 4 items ======= in 0.12 seconds ======== Note that we told ``metafunc.parametrize()`` that your scenario values should be considered class-scoped. With pytest-2.3 this leads to a resource-based ordering. Deferring the setup of parametrized resources --------------------------------------------------- .. regendoc:wipe The parametrization of test functions happens at collection time. It is a good idea to setup expensive resources like DB connections or subprocess only when the actual test is run. Here is a simple example how you can achieve that, first the actual test requiring a ``db`` object:: # content of test_backends.py import pytest def test_db_initialized(db): # a dummy test if db.__class__.__name__ == "DB2": pytest.fail("deliberately failing for demo purposes") We can now add a test configuration that generates two invocations of the ``test_db_initialized`` function and also implements a factory that creates a database object for the actual test invocations:: # content of conftest.py import pytest def pytest_generate_tests(metafunc): if 'db' in metafunc.fixturenames: metafunc.parametrize("db", ['d1', 'd2'], indirect=True) class DB1: "one database object" class DB2: "alternative database object" @pytest.fixture def db(request): if request.param == "d1": return DB1() elif request.param == "d2": return DB2() else: raise ValueError("invalid internal test config") Let's first see how it looks like at collection time:: $ py.test test_backends.py --collect-only ======= test session starts ======== platform linux2 -- Python 2.7.10, pytest-2.8.1.dev1, py-1.4.30, pluggy-0.3.1 rootdir: $REGENDOC_TMPDIR, inifile: collected 2 items ======= in 0.12 seconds ======== And then when we run the test:: $ py.test -q test_backends.py .F ======= FAILURES ======== _______ test_db_initialized[d2] ________ db = def test_db_initialized(db): # a dummy test if db.__class__.__name__ == "DB2": > pytest.fail("deliberately failing for demo purposes") E Failed: deliberately failing for demo purposes test_backends.py:6: Failed 1 failed, 1 passed in 0.12 seconds The first invocation with ``db == "DB1"`` passed while the second with ``db == "DB2"`` failed. Our ``db`` fixture function has instantiated each of the DB values during the setup phase while the ``pytest_generate_tests`` generated two according calls to the ``test_db_initialized`` during the collection phase. .. regendoc:wipe Apply indirect on particular arguments --------------------------------------------------- Very often parametrization uses more than one argument name. There is opportunity to apply ``indirect`` parameter on particular arguments. It can be done by passing list or tuple of arguments' names to ``indirect``. In the example below there is a function ``test_indirect`` which uses two fixtures: ``x`` and ``y``. Here we give to indirect the list, which contains the name of the fixture ``x``. The indirect parameter will be applied to this argument only, and the value ``a`` will be passed to respective fixture function. # content of test_indirect_list.py import pytest @pytest.fixture(scope='function') def x(request): return request.param * 3 @pytest.fixture(scope='function') def y(request): return request.param * 2 @pytest.mark.parametrize('x, y', [('a', 'b')], indirect=['x']) def test_indirect(x,y): assert x == 'aaa' assert y == 'b' The result of this test will be successful: $ py.test test_indirect_list.py --collect-only ======= test session starts ======== platform linux2 -- Python 2.7.10, pytest-2.8.1.dev1, py-1.4.30, pluggy-0.3.1 rootdir: $REGENDOC_TMPDIR, inifile: collected 1 items ======= in 0.12 seconds ======== .. regendoc:wipe Parametrizing test methods through per-class configuration -------------------------------------------------------------- .. _`unittest parameterizer`: http://code.google.com/p/unittest-ext/source/browse/trunk/params.py Here is an example ``pytest_generate_function`` function implementing a parametrization scheme similar to Michael Foord's `unittest parameterizer`_ but in a lot less code:: # content of ./test_parametrize.py import pytest def pytest_generate_tests(metafunc): # called once per each test function funcarglist = metafunc.cls.params[metafunc.function.__name__] argnames = list(funcarglist[0]) metafunc.parametrize(argnames, [[funcargs[name] for name in argnames] for funcargs in funcarglist]) class TestClass: # a map specifying multiple argument sets for a test method params = { 'test_equals': [dict(a=1, b=2), dict(a=3, b=3), ], 'test_zerodivision': [dict(a=1, b=0), ], } def test_equals(self, a, b): assert a == b def test_zerodivision(self, a, b): pytest.raises(ZeroDivisionError, "a/b") Our test generator looks up a class-level definition which specifies which argument sets to use for each test function. Let's run it:: $ py.test -q F.. ======= FAILURES ======== _______ TestClass.test_equals[2-1] ________ self = , a = 1, b = 2 def test_equals(self, a, b): > assert a == b E assert 1 == 2 test_parametrize.py:18: AssertionError 1 failed, 2 passed in 0.12 seconds Indirect parametrization with multiple fixtures -------------------------------------------------------------- Here is a stripped down real-life example of using parametrized testing for testing serialization of objects between different python interpreters. We define a ``test_basic_objects`` function which is to be run with different sets of arguments for its three arguments: * ``python1``: first python interpreter, run to pickle-dump an object to a file * ``python2``: second interpreter, run to pickle-load an object from a file * ``obj``: object to be dumped/loaded .. literalinclude:: multipython.py Running it results in some skips if we don't have all the python interpreters installed and otherwise runs all combinations (5 interpreters times 5 interpreters times 3 objects to serialize/deserialize):: . $ py.test -rs -q multipython.py ssssssssssss...ssssssssssss ======= short test summary info ======== SKIP [12] $REGENDOC_TMPDIR/CWD/multipython.py:22: 'python2.6' not found SKIP [12] $REGENDOC_TMPDIR/CWD/multipython.py:22: 'python3.3' not found 3 passed, 24 skipped in 0.12 seconds Indirect parametrization of optional implementations/imports -------------------------------------------------------------------- If you want to compare the outcomes of several implementations of a given API, you can write test functions that receive the already imported implementations and get skipped in case the implementation is not importable/available. Let's say we have a "base" implementation and the other (possibly optimized ones) need to provide similar results:: # content of conftest.py import pytest @pytest.fixture(scope="session") def basemod(request): return pytest.importorskip("base") @pytest.fixture(scope="session", params=["opt1", "opt2"]) def optmod(request): return pytest.importorskip(request.param) And then a base implementation of a simple function:: # content of base.py def func1(): return 1 And an optimized version:: # content of opt1.py def func1(): return 1.0001 And finally a little test module:: # content of test_module.py def test_func1(basemod, optmod): assert round(basemod.func1(), 3) == round(optmod.func1(), 3) If you run this with reporting for skips enabled:: $ py.test -rs test_module.py ======= test session starts ======== platform linux2 -- Python 2.7.10, pytest-2.8.1.dev1, py-1.4.30, pluggy-0.3.1 rootdir: $REGENDOC_TMPDIR, inifile: collected 2 items test_module.py .s ======= short test summary info ======== SKIP [1] $REGENDOC_TMPDIR/conftest.py:10: could not import 'opt2' ======= 1 passed, 1 skipped in 0.12 seconds ======== You'll see that we don't have a ``opt2`` module and thus the second test run of our ``test_func1`` was skipped. A few notes: - the fixture functions in the ``conftest.py`` file are "session-scoped" because we don't need to import more than once - if you have multiple test functions and a skipped import, you will see the ``[1]`` count increasing in the report - you can put :ref:`@pytest.mark.parametrize <@pytest.mark.parametrize>` style parametrization on the test functions to parametrize input/output values as well.