I am looking to automate the process where:
I run some python code,
then run a set of tests using pytest
then, if all tests are validated, start the process again with new data.
I am thinking of writing a script executing the python code, then calling pytest using pytest.main(), check with the help of the exit code that all tests passed and in case of success start again.
The issue is that it is stated in pytest docs (https://docs.pytest.org/en/stable/usage.html) that it is not recommended to make multiple calls to pytest.main():
Note from pytest docs:
"Calling pytest.main() will result in importing your tests and any modules that they import. Due to the caching mechanism of python’s import system, making subsequent calls to pytest.main() from the same process will not reflect changes to those files between the calls. For this reason, making multiple calls to pytest.main() from the same process (in order to re-run tests, for example) is not recommended."
I was woundering if it was ok to call pytest.main() the way I intend to or if there was any better way to achieve what I am looking for?
I've made a simple example to make it problem more clear:
A = [0]
def some_action(x):
x[0] += 1
if __name__ == '__main__':
print('Initial value of A: {}'.format(A))
for i in range(10):
if i == 5:
# one test in test_mock2 that fails
test_dir = "./tests/functional_tests/test_mock2.py"
else:
# two tests in test_mock that pass
test_dir = "./tests/functional_tests/test_mock.py"
some_action(A)
check_tests = int(pytest.main(["-q", "--tb=no", test_dir]))
if check_tests != 0:
print('Interrupted at i={} because of tests failures'.format(i))
break
if i > 5:
print('All tests validated, final value of A: {}'.format(A))
else:
print('final value of A: {}'.format(A))
In this example some_action is executed until i reaches 5, at which point the tests fail and the process of executing/testing is interrupted. It seems to work fine, I'm only concerned because of the comments in pytest docs as stated above
The warning applies to the following sequence of events:
Run pytest.main on some folder which imports a.py, directly or indirectly.
Modify a.py (manually or programatically).
Attempt to rerun pytest.main on the same directory in the same python process as #1
The second run in step #3 will not not see the changes you made to a.py in step #2. That is because python does not import a file twice. Instead, it will check if the file has an entry in sys.modules, and use that instead. It's what lets you import large libraries multiple times without incurring a huge penalty every time.
Modifying the values in imported modules is fine. Python binds names to references, so if you bind something (like a new integer value) to the right name, everyone will be able to see it. Your some_action function is a good example of this. Future tests will run with the modified value if they import your script as a module.
The reason that the caveat is there is that pytest is usually used to test code after it has been modified. The warning is simply telling you that if you modify your code, you need to start pytest.main in a new python process to see the changes.
Since you do not appear to be modifying the code of the files in your test and expecting the changes to show up, the caveat you cite does not apply to you. Keep doing what you are doing.
CAUTION: This situation was happened because of a mistake. Check my answer.
I have a python file (myfile.py) which I want to test its content with Pytest. (The content is changing dynamically)
I've wrote this code:
import importlib
def test_myfile(capsys, monkeypatch):
monkeypatch.setattr('builtins.input', lambda s: "some_input")
# Create a module from the custom Problem file and import it
my_module = importlib.import_module("myfile")
# Rest of the test script
when I run the test I'm getting this error:
OSError: reading from stdin while output is captured
The error has been produced because there is an input() instruction in myfile.py and it means that mocking that function was futile.
My Question:
How can I mock out some functions inside a module I want to import?
Finally I found that I was looking wrong place to find a solution.
actually I use pytest to check a learner response and grade it. this was the way I did that at the moment I asked this question.
py.test test_runner.py test_case.py -v
This will test the user response which is saved in test_case.py ( I get the second parameter inside my test method and load its content and for example run a desired function ). Then I was examining the report of pytest to see if there was an error or failure to decide about the result. (pass/failure)
normally pytest was failing if there was an error in users code (e.g. syntax error) or if the test was failing (e.g. didn't return what it must return)
This time there was an error I didn't want to stop the test. I want to mock out input() function in users code. when I was running the command, before running my test method, pytest imported both files to collect test methods. It was failing to import test_case.py because of the input() function. It was not even reach the line I asked to mock that function and failed in the init stage.
At last to fix the problem I've added a parameter to py.test and now I'm running test process like this:
py.test test_runner.py --program test_case.py -v
In this form pytest doesn't look in test_case.py for test methods and doesn't fail.
hope this experience helps someone else
I have a Python package (Python 3.6, if it makes a difference) that I've designed to run as 'python -m package arguments' and I'd like to write unit tests for the __main__.py module. I specifically want to verify that it sets the exit code correctly. Is it possible to use runpy.run_module to execute my __main__.py and test the exit code? If so, how do I retrieve the exit code?
To be more clear, my __main__.py module is very simple. It just calls a function that has been extensively unit tested. But when I originally wrote __main__.py, I forgot to pass the result of that function to exit(), so I would like unit tests where the main function is mocked to make sure the exit code is set correctly. My unit test would look something like:
#patch('my_module.__main__.my_main', return_value=2)
def test_rc2(self, _):
"""Test that rc 2 is the exit code."""
sys.argv = ['arg0', 'arg1', 'arg2', …]
runpy.run_module('my_module')
self.assertEqual(mod_rc, 2)
My question is, how would I get what I’ve written here as ‘mod_rc’?
Thanks.
Misko Hevery has said before (I believe it was in Clean Code Talks: Don't Look for Things but I may be wrong) that he doesn't know how to effectively unit test main methods, so his solution is to make them so simple that you can prove logically that they work if you assume the correctness of the (unit-tested) code that they call.
For example, if you have a discrete, tested unit for parsing command line arguments; a library that does the actual work; and a discrete, tested unit for rendering the completed work into output, then a main method that calls all three of those in sequence is assuredly going to work.
With that architecture, you can basically get by with just one big system test that is expected to produce something other than the "default" output and it'll either crash (because you wired it up improperly) or work (because it's wired up properly and all of the individual parts work).
At this point, I'm dropping all pretense of knowing what I'm talking about. There is almost assuredly a better way to do this, but frankly you could just write a shell script:
python -m package args
test $? -eq [expected exit code]
That will exit with error iff your program outputs incorrectly, which TravisCI or similar will regard as build failing.
__main__.py is still subject to normal __main__ global behavior — which is to say, you can implement your __main__.py like so
def main():
# Your stuff
if __name__ == "__main__":
main()
and then you can test your __main__ in whatever testing framework you like by using
from your_package.__main__ import main
As an aside, if you are using argparse, you will probably want:
def main(arg_strings=None):
# …
args = parser.parse_args(arg_strings)
# …
if __name__ == "__main__":
main()
and then you can override arg strings from a unit test simply with
from your_package.__main__ import main
def test_main():
assert main(["x", "y", "z"]) == …
or similar idiom in you testing framework.
With pytest, I was able to do:
import mypkgname.__main__ as rtmain
where mypkgname is what you've named your app as a package/module. Then just running pytest as normal worked. I hope this helps some other poor soul.
Running: Windows 7, python 3.4 & 2.7
In one of my nosetests plugin, (one that post test data to a website), I need to ascertain if the test is being run with the --failed option or without. If --failed is enabled, that means this test failed the first time and is being run once more to see if that fail was a fluke. If this is a re-run of a failed test I need to direct my plugin to some different behavior vs. if the test is being run for the first time.
In other words, I want to ascertain inside the plugin if we are inside nosetests or nosetests --failed.
How can I access nosetest's command line options from inside a plug in? What variable are the options stored in?
My eventual code will look something like this:
if <--failed option was invoked with nosetests command>:
do something
else:
do something different
What's the correct code to replace what's inside <>?
I don't fully understand, but the command line arguments part is easy. Just use the following code:
from sys import argv as arguments
if "--failed" in arguments :
do_something()
else :
do_something_else()
When you import sys, you have access to the sys.argv
The simplest way to grab command line arguments is the system library
import sys
sys.argv #this is a list of args sys.argv[0] is the program itself
so it would be
if sys.argv[1] == '--failed':
I am working on a python Command-Line-Interface program, and I find it boring when doing testings, for example, here is the help information of the program:
usage: pyconv [-h] [-f ENCODING] [-t ENCODING] [-o file_path] file_path
Convert text file from one encoding to another.
positional arguments:
file_path
optional arguments:
-h, --help show this help message and exit
-f ENCODING, --from ENCODING
Encoding of source file
-t ENCODING, --to ENCODING
Encoding you want
-o file_path, --output file_path
Output file path
When I made changes on the program and want to test something, I must open a terminal,
type the command(with options and arguments), type enter, and see if any error occurs
while running. If error really occurs, I must go back to the editor and check the code
from top to end, guessing where the bug positions, make small changes, write print lines,
return to the terminal, run command again...
Recursively.
So my question is, what is the best way to do testing with CLI program, can it be as easy
as unit testing with normal python scripts?
I think it's perfectly fine to test functionally on a whole-program level. It's still possible to test one aspect/option per test. This way you can be sure that the program really works as a whole. Writing unit-tests usually means that you get to execute your tests quicker and that failures are usually easier to interpret/understand. But unit-tests are typically more tied to the program structure, requiring more refactoring effort when you internally change things.
Anyway, using py.test, here is a little example for testing a latin1 to utf8 conversion for pyconv::
# content of test_pyconv.py
import pytest
# we reuse a bit of pytest's own testing machinery, this should eventually come
# from a separatedly installable pytest-cli plugin.
pytest_plugins = ["pytester"]
#pytest.fixture
def run(testdir):
def do_run(*args):
args = ["pyconv"] + list(args)
return testdir._run(*args)
return do_run
def test_pyconv_latin1_to_utf8(tmpdir, run):
input = tmpdir.join("example.txt")
content = unicode("\xc3\xa4\xc3\xb6", "latin1")
with input.open("wb") as f:
f.write(content.encode("latin1"))
output = tmpdir.join("example.txt.utf8")
result = run("-flatin1", "-tutf8", input, "-o", output)
assert result.ret == 0
with output.open("rb") as f:
newcontent = f.read()
assert content.encode("utf8") == newcontent
After installing pytest ("pip install pytest") you can run it like this::
$ py.test test_pyconv.py
=========================== test session starts ============================
platform linux2 -- Python 2.7.3 -- pytest-2.4.5dev1
collected 1 items
test_pyconv.py .
========================= 1 passed in 0.40 seconds =========================
The example reuses some internal machinery of pytest's own testing by leveraging pytest's fixture mechanism, see http://pytest.org/latest/fixture.html. If you forget about the details for a moment, you can just work from the fact that "run" and "tmpdir" are provided for helping you to prepare and run tests. If you want to play, you can try to insert a failing assert-statement or simply "assert 0" and then look at the traceback or issue "py.test --pdb" to enter a python prompt.
Start from the user interface with functional tests and work down towards unit tests. It can feel difficult, especially when you use the argparse module or the click package, which take control of the application entry point.
The cli-test-helpers Python package has examples and helper functions (context managers) for a holistic approach on writing tests for your CLI. It's a simple idea, and one that works perfectly with TDD:
Start with functional tests (to ensure your user interface definition) and
Work towards unit tests (to ensure your implementation contracts)
Functional tests
NOTE: I assume you develop code that is deployed with a setup.py file or is run as a module (-m).
Is the entrypoint script installed? (tests the configuration in your setup.py)
Can this package be run as a Python module? (i.e. without having to be installed)
Is command XYZ available? etc. Cover your entire CLI usage here!
Those tests are simplistic: They run the shell command you would enter in the terminal, e.g.
def test_entrypoint():
exit_status = os.system('foobar --help')
assert exit_status == 0
Note the trick to use a non-destructive operation (e.g. --help or --version) as we can't mock anything with this approach.
Towards unit tests
To test single aspects inside the application you will need to mimic things like command line arguments and maybe environment variables. You will also need to catch the exiting of your script to avoid the tests to fail for SystemExit exceptions.
Example with ArgvContext to mimic command line arguments:
#patch('foobar.command.baz')
def test_cli_command(mock_command):
"""Is the correct code called when invoked via the CLI?"""
with ArgvContext('foobar', 'baz'), pytest.raises(SystemExit):
foobar.cli.main()
assert mock_command.called
Note that we mock the function that we want our CLI framework (click in this example) to call, and that we catch SystemExit that the framework naturally raises. The context managers are provided by cli-test-helpers and pytest.
Unit tests
The rest is business as usual. With the above two strategies we've overcome the control a CLI framework may have taken away from us. The rest is usual unit testing. TDD-style hopefully.
Disclosure: I am the author of the cli-test-helpers Python package.
So my question is, what is the best way to do testing with CLI program, can it be as easy as unit testing with normal python scripts?
The only difference is that when you run Python module as a script, its __name__ attribute is set to '__main__'. So generally, if you intend to run your script from command line it should have following form:
import sys
# function and class definitions, etc.
# ...
def foo(arg):
pass
def main():
"""Entry point to the script"""
# Do parsing of command line arguments and other stuff here. And then
# make calls to whatever functions and classes that are defined in your
# module. For example:
foo(sys.argv[1])
if __name__ == '__main__':
main()
Now there is no difference, how you would use it: as a script or as a module. So inside your unit-testing code you can just import foo function, call it and make any assertions you want.
Maybe too little too late,
but you can always use
import os.system
result = os.system(<'Insert your command with options here'>
assert(0 == result)
In that way, you can run your program as if it was from command line, and evaluate the exit code.
(Update after I studied pytest)
You can also use capsys.
(from running pytest --fixtures)
capsys
Enable text capturing of writes to sys.stdout and sys.stderr.
The captured output is made available via ``capsys.readouterr()`` method
calls, which return a ``(out, err)`` namedtuple.
``out`` and ``err`` will be ``text`` objects.
This isn't for Python specifically, but what I do to test command-line scripts is to run them with various predetermined inputs and options and store the correct output in a file. Then, to test them when I make changes, I simply run the new script and pipe the output into diff correct_output -. If the files are the same, it outputs nothing. If they're different, it shows you where. This will only work if you are on Linux or OS X; on Windows, you will have to get MSYS.
Example:
python mycliprogram --someoption "some input" | diff correct_output -
To make it even easier, you can add all these test runs to your 'make test' Makefile target, which I assume you already have. ;)
If you are running many of these at once, you could make it a little more obvious where each one ends by adding a fail tag:
python mycliprogram --someoption "some input" | diff correct_output - || tput setaf 1 && echo "FAILED"
The short answer is yes, you can use unit tests, and should. If your code is well structured, it should be quite easy to test each component separately, and if you need to to can always mock sys.argv to simulate running it with different arguments.
pytest-console-scripts is a Pytest plugin for testing python scripts installed via console_scripts entry point of setup.py.
For Python 3.5+, you can use the simpler subprocess.run to call your CLI command from your test.
Using pytest:
import subprocess
def test_command__works_properly():
try:
result = subprocess.run(['command', '--argument', 'value'], check=True, capture_output=True, text=True)
except subprocess.CalledProcessError as error:
print(error.stdout)
print(error.stderr)
raise error
The output can be accessed via result.stdout, result.stderr, and result.returncode if needed.
The check parameter causes an exception to be raised if an error occurs. Note Python 3.7+ is required for the capture_output and text parameters, which simplify capturing and reading stdout/stderr.
Given that you are explicitly asking about testing for a command line application, I believe that you are aware of unit-testing tools in python and that you are actually looking for a tool to automate end-to-end tests of a command line tool. There are a couple of tools out there that are specifically designed for that. If you are looking for something that's pip-installable, I would recommend cram. It integrates well with the rest of the python environment (e.g. through a pytest extension) and it's quite easy to use:
Simply write the commands you want to run prepended with $ and the expected output prepended with . For example, the following would be a valid cram test:
$ echo Hello
Hello
By having four spaces in front of expected output and two in front of the test, you can actually use these tests to also write documentation. More on that on the website.
You can use standard unittest module:
# python -m unittest <test module>
or use nose as a testing framework. Just write classic unittest files in separate directory and run:
# nosetests <test modules directory>
Writing unittests is easy. Just follow online manual for unittesting
I would not test the program as a whole this is not a good test strategy and may not actually catch the actual spot of the error. The CLI interface is just front end to an API. You test the API via your unit tests and then when you make a change to a specific part you have a test case to exercise that change.
So, restructure your application so that you test the API and not the application it self. But, you can have a functional test that actually does run the full application and checks that the output is correct.
In short, yes testing the code is the same as testing any other code, but you must test the individual parts rather than their combination as a whole to ensure that your changes do not break everything.