.. _paramexamples:
Parametrizing tests
=================================================
.. currentmodule:: _pytest.python
py.test 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
..
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
As expected when running the full range of ``param1`` values
we'll get an error on the last one.
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 2.7.3 -- pytest-2.3.5
collected 4 items
test_scenarios.py ....
========================= 4 passed in 0.01 seconds =========================
If you just collect tests you'll also nicely see 'advanced' and 'basic' as variants for the test function::
$ py.test --collectonly test_scenarios.py
=========================== test session starts ============================
platform linux2 -- Python 2.7.3 -- pytest-2.3.5
collected 4 items
<Module 'test_scenarios.py'>
<Class 'TestSampleWithScenarios'>
<Instance '()'>
<Function 'test_demo1[basic]'>
<Function 'test_demo2[basic]'>
<Function 'test_demo1[advanced]'>
<Function 'test_demo2[advanced]'>
============================= in 0.01 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 --collectonly
=========================== test session starts ============================
platform linux2 -- Python 2.7.3 -- pytest-2.3.5
collected 2 items
<Module 'test_backends.py'>
<Function 'test_db_initialized[d1]'>
<Function 'test_db_initialized[d2]'>
============================= in 0.00 seconds =============================
And then when we run the test::
$ py.test -q test_backends.py
.F
================================= FAILURES =================================
_________________________ test_db_initialized[d2] __________________________
db = <conftest.DB2 instance at 0x2038f80>
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
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
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[1-2] ________________________
self = <test_parametrize.TestClass instance at 0x1338f80>, a = 1, b = 2
def test_equals(self, a, b):
> assert a == b
E assert 1 == 2
test_parametrize.py:18: AssertionError
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
............sss............sss............sss............ssssssssssssssssss
========================= short test summary info ==========================
SKIP [27] /home/hpk/p/pytest/doc/en/example/multipython.py:21: 'python2.8' not found
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 2.7.3 -- pytest-2.3.5
collected 2 items
test_module.py .s
========================= short test summary info ==========================
SKIP [1] /tmp/doc-exec-275/conftest.py:10: could not import 'opt2'
=================== 1 passed, 1 skipped in 0.01 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.
