Suppose we want to write a test that depends on a command line option. Here is a basic pattern how to achieve this:
# content of test_sample.py
def test_answer(cmdopt):
if cmdopt == "type1":
print ("first")
elif cmdopt == "type2":
print ("second")
assert 0 # to see what was printed
For this to work we need to add a command line option and provide the cmdopt through a function argument factory:
# content of conftest.py
def pytest_addoption(parser):
parser.addoption("--cmdopt", action="store", default="type1",
help="my option: type1 or type2")
def pytest_funcarg__cmdopt(request):
return request.config.option.cmdopt
Let’s run this without supplying our new command line option:
$ py.test -q test_sample.py
collecting ... collected 1 items
F
================================= FAILURES =================================
_______________________________ test_answer ________________________________
cmdopt = 'type1'
def test_answer(cmdopt):
if cmdopt == "type1":
print ("first")
elif cmdopt == "type2":
print ("second")
> assert 0 # to see what was printed
E assert 0
test_sample.py:6: AssertionError
----------------------------- Captured stdout ------------------------------
first
1 failed in 0.50 seconds
And now with supplying a command line option:
$ py.test -q --cmdopt=type2
collecting ... collected 1 items
F
================================= FAILURES =================================
_______________________________ test_answer ________________________________
cmdopt = 'type2'
def test_answer(cmdopt):
if cmdopt == "type1":
print ("first")
elif cmdopt == "type2":
print ("second")
> assert 0 # to see what was printed
E assert 0
test_sample.py:6: AssertionError
----------------------------- Captured stdout ------------------------------
second
1 failed in 0.02 seconds
Ok, this completes the basic pattern. However, one often rather wants to process command line options outside of the test and rather pass in different or more complex objects. See the next example or refer to Mysetup pattern: application specific test fixtures for more information on real-life examples.
Through addopts you can statically add command line options for your project. You can also dynamically modify the command line arguments before they get processed:
# content of conftest.py
import sys
def pytest_cmdline_preparse(args):
if 'xdist' in sys.modules: # pytest-xdist plugin
import multiprocessing
num = max(multiprocessing.cpu_count() / 2, 1)
args[:] = ["-n", str(num)] + args
If you have the xdist plugin installed you will now always perform test runs using a number of subprocesses close to your CPU. Running in an empty directory with the above conftest.py:
$ py.test
=========================== test session starts ============================
platform darwin -- Python 2.7.1 -- pytest-2.2.2
gw0 I
gw0 [0]
scheduling tests via LoadScheduling
============================= in 5.12 seconds =============================
Here is a conftest.py file adding a --runslow command line option to control skipping of slow marked tests:
# content of conftest.py
import pytest
def pytest_addoption(parser):
parser.addoption("--runslow", action="store_true",
help="run slow tests")
def pytest_runtest_setup(item):
if 'slow' in item.keywords and not item.config.getvalue("runslow"):
pytest.skip("need --runslow option to run")
We can now write a test module like this:
# content of test_module.py
import pytest
slow = pytest.mark.slow
def test_func_fast():
pass
@slow
def test_func_slow():
pass
and when running it will see a skipped “slow” test:
$ py.test -rs # "-rs" means report details on the little 's'
=========================== test session starts ============================
platform darwin -- Python 2.7.1 -- pytest-2.2.2
collecting ... collected 2 items
test_module.py .s
========================= short test summary info ==========================
SKIP [1] /Users/hpk/tmp/doc-exec-158/conftest.py:9: need --runslow option to run
=================== 1 passed, 1 skipped in 0.09 seconds ====================
Or run it including the slow marked test:
$ py.test --runslow
=========================== test session starts ============================
platform darwin -- Python 2.7.1 -- pytest-2.2.2
collecting ... collected 2 items
test_module.py ..
========================= 2 passed in 0.02 seconds =========================
If you have a test helper function called from a test you can use the pytest.fail marker to fail a test with a certain message. The test support function will not show up in the traceback if you set the __tracebackhide__ option somewhere in the helper function. Example:
# content of test_checkconfig.py
import pytest
def checkconfig(x):
__tracebackhide__ = True
if not hasattr(x, "config"):
pytest.fail("not configured: %s" %(x,))
def test_something():
checkconfig(42)
The __tracebackhide__ setting influences py.test showing of tracebacks: the checkconfig function will not be shown unless the --fulltrace command line option is specified. Let’s run our little function:
$ py.test -q test_checkconfig.py
collecting ... collected 1 items
F
================================= FAILURES =================================
______________________________ test_something ______________________________
def test_something():
> checkconfig(42)
E Failed: not configured: 42
test_checkconfig.py:8: Failed
1 failed in 0.07 seconds
Usually it is a bad idea to make application code behave differently if called from a test. But if you absolutely must find out if your application code is running from a test you can do something like this:
# content of conftest.py
def pytest_configure(config):
import sys
sys._called_from_test = True
def pytest_unconfigure(config):
del sys._called_from_test
and then check for the sys._called_from_test flag:
if hasattr(sys, '_called_from_test'):
# called from within a test run
else:
# called "normally"
accordingly in your application. It’s also a good idea to use your own application module rather than sys for handling flag.
It’s easy to present extra information in a py.test run:
# content of conftest.py
def pytest_report_header(config):
return "project deps: mylib-1.1"
which will add the string to the test header accordingly:
$ py.test
=========================== test session starts ============================
platform darwin -- Python 2.7.1 -- pytest-2.2.2
project deps: mylib-1.1
collecting ... collected 0 items
============================= in 0.01 seconds =============================
You can also return a list of strings which will be considered as several lines of information. You can of course also make the amount of reporting information on e.g. the value of config.option.verbose so that you present more information appropriately:
# content of conftest.py
def pytest_report_header(config):
if config.option.verbose > 0:
return ["info1: did you know that ...", "did you?"]
which will add info only when run with “–v”:
$ py.test -v
=========================== test session starts ============================
platform darwin -- Python 2.7.1 -- pytest-2.2.2 -- /Users/hpk/venv/0/bin/python
info1: did you know that ...
did you?
collecting ... collected 0 items
============================= in 0.03 seconds =============================
and nothing when run plainly:
$ py.test
=========================== test session starts ============================
platform darwin -- Python 2.7.1 -- pytest-2.2.2
collecting ... collected 0 items
============================= in 0.01 seconds =============================
If you have a slow running large test suite you might want to find out which tests are the slowest. Let’s make an artifical test suite:
# content of test_some_are_slow.py
import time
def test_funcfast():
pass
def test_funcslow1():
time.sleep(0.1)
def test_funcslow2():
time.sleep(0.2)
Now we can profile which test functions execute the slowest:
$ py.test --durations=3
=========================== test session starts ============================
platform darwin -- Python 2.7.1 -- pytest-2.2.2
collecting ... collected 3 items
test_some_are_slow.py ...
========================= slowest 3 test durations =========================
0.20s call test_some_are_slow.py::test_funcslow2
0.10s call test_some_are_slow.py::test_funcslow1
0.00s call test_some_are_slow.py::test_funcfast
========================= 3 passed in 0.33 seconds =========================