test_ok2/doc/en/parametrize.txt

.. _`test generators`:
.. _`parametrizing-tests`:
.. _`parametrized test functions`:
.. _`parametrize`:

.. _`parametrize-basics`:

Parametrizing fixtures and test functions
==========================================================================

pytest supports test parametrization in several well-integrated ways:

- :py:func:`pytest.fixture` allows to define :ref:`parametrization
  at the level of fixture functions <fixture-parametrize>`.

* `@pytest.mark.parametrize`_ allows to define parametrization at the
  function or class level, provides multiple argument/fixture sets 
  for a particular test function or class.

* `pytest_generate_tests`_ enables implementing your own custom 
  dynamic parametrization scheme or extensions.

.. _parametrizemark:
.. _`@pytest.mark.parametrize`:


``@pytest.mark.parametrize``: parametrizing test functions
---------------------------------------------------------------------

.. regendoc: wipe

.. versionadded:: 2.2

The builtin ``pytest.mark.parametrize`` decorator enables
parametrization of arguments for a test function.  Here is a typical example
of a test function that implements checking that a certain input leads
to an expected output::

    # content of test_expectation.py
    import pytest
    @pytest.mark.parametrize(("input", "expected"), [
        ("3+5", 8),
        ("2+4", 6),
        ("6*9", 42),
    ])
    def test_eval(input, expected):
        assert eval(input) == expected

Here, the ``@parametrize`` decorator defines three different argument
sets for the two ``(input, output)`` arguments of the ``test_eval`` function
which will thus run three times::

    $ py.test 
    =========================== test session starts ============================
    platform linux2 -- Python 2.7.3 -- pytest-2.3.5
    collected 3 items
    
    test_expectation.py ..F
    
    ================================= FAILURES =================================
    ____________________________ test_eval[6*9-42] _____________________________
    
    input = '6*9', expected = 42
    
        @pytest.mark.parametrize(("input", "expected"), [
            ("3+5", 8),
            ("2+4", 6),
            ("6*9", 42),
        ])
        def test_eval(input, expected):
    >       assert eval(input) == expected
    E       assert 54 == 42
    E        +  where 54 = eval('6*9')
    
    test_expectation.py:8: AssertionError
    ==================== 1 failed, 2 passed in 0.01 seconds ====================

As expected only one pair of input/output values fails the simple test function.
And as usual with test function arguments, you can see the ``input`` and ``output`` values in the traceback.

Note that there ways how you can mark a class or a module,
see :ref:`mark`.

It is also possible to mark individual test instances within parametrize::

    # content of test_expectation.py
    import pytest
    @pytest.mark.parametrize(("input", "expected"), [
        ("3+5", 8),
        ("2+4", 6),
        pytest.mark.xfail(("6*9", 42)),
    ])
    def test_eval(input, expected):
        assert eval(input) == expected


.. _`pytest_generate_tests`:

Basic ``pytest_generate_tests`` example
---------------------------------------------

Sometimes you may want to implement your own parametrization scheme
or implement some dynamism for determining the parameters or scope
of a fixture.   For this, you can use the ``pytest_generate_tests`` hook 
which is called when collecting a test function.  Through the passed in
`metafunc` object you can inspect the requesting test context and, most
importantly, you can call ``metafunc.parametrize()`` to cause
parametrization.  

For example, let's say we want to run a test taking string inputs which
we want to set via a new py.test command line option.  Let's first write 
a simple test accepting a ``stringinput`` fixture function argument::

    # content of test_strings.py

    def test_valid_string(stringinput):
        assert stringinput.isalpha()

Now we add a ``conftest.py`` file containing the addition of a 
command line option and the parametrization of our test function::

    # content of conftest.py

    def pytest_addoption(parser):
        parser.addoption("--stringinput", action="append", default=[],
            help="list of stringinputs to pass to test functions")

    def pytest_generate_tests(metafunc):
        if 'stringinput' in metafunc.fixturenames:
            metafunc.parametrize("stringinput", 
                                 metafunc.config.option.stringinput)

If we now pass two stringinput values, our test will run twice::

    $ py.test -q --stringinput="hello" --stringinput="world" test_strings.py
    ..

Let's also run with a stringinput that will lead to a failing test::

    $ py.test -q --stringinput="!" test_strings.py
    F
    ================================= FAILURES =================================
    ___________________________ test_valid_string[!] ___________________________
    
    stringinput = '!'
    
        def test_valid_string(stringinput):
    >       assert stringinput.isalpha()
    E       assert <built-in method isalpha of str object at 0x2ba729dab300>()
    E        +  where <built-in method isalpha of str object at 0x2ba729dab300> = '!'.isalpha
    
    test_strings.py:3: AssertionError

As expected our test function fails. 

If you don't specify a stringinput it will be skipped because
``metafunc.parametrize()`` will be called with an empty parameter
listlist::

    $ py.test -q -rs test_strings.py 
    s
    ========================= short test summary info ==========================
    SKIP [1] /home/hpk/p/pytest/.tox/regen/local/lib/python2.7/site-packages/_pytest/python.py:974: got empty parameter set, function test_valid_string at /tmp/doc-exec-240/test_strings.py:1

For further examples, you might want to look at :ref:`more
parametrization examples <paramexamples>`.

.. _`metafunc object`:

The **metafunc** object
-------------------------------------------

.. currentmodule:: _pytest.python

metafunc objects are passed to the ``pytest_generate_tests`` hook.
They help to inspect a testfunction and to generate tests
according to test configuration or values specified
in the class or module where a test function is defined:

``metafunc.fixturenames``: set of required function arguments for given function

``metafunc.function``: underlying python test function

``metafunc.cls``: class object where the test function is defined in or None.

``metafunc.module``: the module object where the test function is defined in.

``metafunc.config``: access to command line opts and general config

``metafunc.funcargnames``: alias for ``fixturenames``, for pre-2.3 compatibility

.. automethod:: Metafunc.parametrize
.. automethod:: Metafunc.addcall(funcargs=None,id=_notexists,param=_notexists)