I am working with someone else's testing code, and they make extensive use of mocker. The problem is that I changed the underlying code so it tests for the existence of a file using Path ().is_file.
Now I need to mock Path ().is_file so it returns True. I tried this:
from pathlib import Path
#pytest.fixture(scope="function")
def mock_is_file (mocker):
# mock the AlignDir existence validation
mocker.patch ('Path.is_file')
return True
I'm getting this error:
E ModuleNotFoundError: No module named 'Path'
/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/unittest/mock.py:1161: ModuleNotFoundError
What is the correct way to patch Path.is_file()?
Mock will import the object given the string, so you need to patch with a fully qualified name.
You should probably also set the return_value so that the call returns a boolean, rather than returning another generated mock. The return value in the fixture itself is not needed.
mocker.patch("pathlib.Path.is_file", return_value=True)
You also don't need to import the thing that you're mocking at the top of the test module like that, mock itself will import it when patching.
I have a pytest fixture that imports a specific module. This is needed as importing the module is very expensive, so we don't want to do it on import-time (i.e. during pytest test collection). This results in code like this:
#pytest.fixture
def my_module_fix():
import my_module
yield my_module
def test_something(my_module_fix):
assert my_module_fix.my_func() = 5
I am using PyCharm and would like to have type-checking and autocompletion in my tests. To achieve that, I would somehow have to annotate the my_module_fix parameter as having the type of the my_module module.
I have no idea how to achieve that. All I found is that I can annotate my_module_fix as being of type types.ModuleType, but that is not enough: It is not any module, it is always my_module.
If I get your question, you have two (or three) separate goals
Deferred import of slowmodule
Autocomplete to continue to work as if it was a standard import
(Potentially?) typing (e.g. mypy?) to continue to work
I can think of at least five different approaches, though I'll only briefly mention the last because it's insane.
Import the module inside your tests
This is (by far) the most common and IMHO preferred solution.
e.g. instead of
import slowmodule
def test_foo():
slowmodule.foo()
def test_bar():
slowmodule.bar()
you'd write:
def test_foo():
import slowmodule
slowmodule.foo()
def test_bar():
import slowmodule
slowmodule.bar()
[deferred importing] Here, the module will be imported on-demand/lazily. So if you have pytest setup to fail-fast, and another test fails before pytest gets to your (test_foo, test_bar) tests, the module will never be imported and you'll never incur the runtime cost.
Because of Python's module cache, subsequent import statements won't actually re-import the module, just grab a reference to the already-imported module.
[autocomplete/typing] Of course, autocomplete will continue to work as you expect in this case. This is a perfectly fine import pattern.
While it does require adding potentially many additional import statements (one inside each test function), it's immediately clear what is going on (regardless of whether it's clear why it's going on).
[3.7+] Proxy your module with module __getattr__
If you create a module (e.g. slowmodule_proxy.py) with the contents like:
def __getattr__(name):
import slowmodule
return getattr(slowmodule, name)
And in your tests, e.g.
import slowmodule
def test_foo():
slowmodule.foo()
def test_bar():
slowmodule.bar()
instead of:
import slowmodule
you write:
import slowmodule_proxy as slowmodule
[deferred import] Thanks to PEP-562, you can "request" any name from slowmodule_proxy and it will fetch and return the corresponding name from slowmodule. Just as above, including the import inside the function will cause slowmodule to be imported only when the function is called and executed instead of on module load. Module caching still applies here of course, so you're only incurring the import penalty once per interpreter session.
[autocomplete] However, while deferred importing will work (and your tests run without issue), this approach (as stated so far) will "break" autocomplete:
Now we're in the realm of PyCharm. Some IDEs will perform "live" analysis of modules and actually load up the module and inspect its members. (PyDev had this option). If PyCharm did this, implementing module.__dir__ (same PEP) or __all__ would allow your proxy module to masquerade as the actual slowmodule and autocomplete would work.† But, PyCharm does not do this.
Nonetheless, you can fool PyCharm into giving you autocomplete suggestions:
if False:
import slowmodule
else:
import slowmodule_proxy as slowmodule
The interpreter will only execute the else branch, importing the proxy and naming it slowmodule (so your test code can continue to reference slowmodule unchanged).
But PyCharm will now provide autocompletion for the underlying module:
† While live-analysis can be an incredibly helpful, there's also a (potential) security concern that comes with it that static syntax analysis doesn't have. And the maturation of type hinting and stub files has made it less of an issue still.
Proxy slowmodule explicitly
If you really hated the dynamic proxy approach (or the fact that you have to fool PyCharm in this way), you could proxy the module explicitly.
(You'd likely only want to consider this if the slowmodule API is stable.)
If slowmodule has methods foo and bar you'd create a proxy module like:
def foo(*args, **kwargs):
import slowmodule
return slowmodule.foo(*args, **kwargs)
def bar(*args, **kwargs):
import slowmodule
return slowmodule.bar(*args, **kwargs)
(Using args and kwargs to pass arguments through to the underlying callables. And you could add type hinting to these functions to mirror the slowmodule functions.)
And in your test,
import slowmodule_proxy as slowmodule
Same as before. Importing inside the method gives you the deferred importing you want and the module cache takes care of multiple import calls.
And since it's a real module whose contents can be statically analyzed, there's no need to "fool" PyCharm.
So the benefit of this solution is that you don't have a bizarre looking if False in your test imports. This, however, comes at the (substantial) cost of having to maintain a proxy file alongside your module -- which could prove painful in the case that slowmodule's API wasn't stable.
[3.5+] Use importlib's LazyLoader instead of a proxy module
Instead of the proxy module slowmodule_proxy, you could follow a pattern similar to the one shown in the importlib docs
>>> import importlib.util
>>> import sys
>>> def lazy_import(name):
... spec = importlib.util.find_spec(name)
... loader = importlib.util.LazyLoader(spec.loader)
... spec.loader = loader
... module = importlib.util.module_from_spec(spec)
... sys.modules[name] = module
... loader.exec_module(module)
... return module
...
>>> lazy_typing = lazy_import("typing")
>>> #lazy_typing is a real module object,
>>> #but it is not loaded in memory yet.
You'd still need to fool PyCharm though, so something like:
if False:
import slowmodule
else:
slowmodule = lazy_import('slowmodule')
would be necessary.
Outside of the single additional level of indirection on module member access (and the two minor version availability difference), it's not immediately clear to me what, if anything, there is to be gained from this approach over the previous proxy module method, however.
Use importlib's Finder/Loader machinery to hook import (don't do this)
You could create a custom module Finder/Loader that would (only) hook your slowmodule import and, instead load, for example your proxy module.
Then you could just import that "importhook" module before you imported slowmode in your tests, e.g.
import myimporthooks
import slowmodule
def test_foo():
...
(Here, myimporthooks would use importlib's finder and loader machinery to do something simlar to the importhook package but intercept and redirect the import attempt rather than just serving as an import callback.)
But this is crazy. Not only is what you want (seemingly) achievable through (infinitely) more common and supported methods, but it's incredibly fragile, error-prone and, without diving into the internals of PyTest (which may mess with module loaders itself), it's hard to say whether it'd even work.
When Pytest collects files to be tested, modules are only imported once, even if the same import statement appears in multiple files.
To observe when my_module is imported, add a print statement and then use the Pytest -s flag (short for --capture=no), to ensure that all standard output is displayed.
my_module.py
answer: int = 42
print("MODULE IMPORTED: my_module.py")
You could then add your test fixture to a conftest.py file:
conftest.py
import pytest
#pytest.fixture
def my_module_fix():
import my_module
yield my_module
Then in your test files, my_module.py may be imported to add type hints:
test_file_01.py
import my_module
def test_something(my_module_fix: my_module):
assert my_module_fix.answer == 42
test_file_02.py
import my_module
def test_something2(my_module_fix: my_module):
assert my_module_fix.answer == 42
Then run Pytest to display all standard output and verify that the module is only imported once at runtime.
pytest -s ./
Output from Pytest
platform linux -- Python 3.9.7, pytest-6.2.5
rootdir: /home/your_username/your_repo
collecting ... MODULE IMPORTED: my_module.py <--- Print statement executed once
collected 2 items
test_file_01.py .
test_file_02.py .
This is quick and naive, but can you possibly annotate your tests with the "quote-style" annotation that is meant for different purposes, but may suit here by skipping the import at runtime but still help your editor?
def test_something(my_module_fix: "my_module"):
In a quick test, this seems to accomplish it at least for my setup.
Although it might not be considered a 'best practice', to keep your specific use case simple, you could just lazily import the module directly in your test where you need it.
def test_something():
import my_module
assert my_module.my_func() = 5
I believe Pytest will only import the module when the applicable tests run. Pytest should also 'cache' the import as well so that if multiple tests import the module, it is only actually imported once. This may also solve your autocomplete issues for your editor.
Side-note: Avoid writing code in a specific way to cater for a specific editor. Keep it simple, not everyone who looks at your code will use Pycharm.
would like to have type-checking and autocompletion in my tests
It sounds like you want Something that fits as the type symbol of your test function:
def test_something(my_module_fix: Something):
assert my_module_fix.my_func() = 5
... and from this, [hopefully] your IDE can make some inferences about my_module_fix. I'm not a PyCharm user, so I can't speak to what it can tell you from type signatures, but I can say this isn't something that's readily available.
For some intuition, in this example -- Something is a ModuleType like 3 is an int. Analogously, accessing a nonexistent attribute of Something is like doing something not allowed with 3. Perhaps, like accessing an attribute of it (3.__name__).
But really, I seems like you're thinking about this from the wrong direction. The question a type signature answers is: what contract must this [these] argument[s] satisfy for this function to use it [them]. Using the example above, a type of 3 is the kind of thing that is too specific to make useful functions:
def add_to(i: 3):
return i
Perhaps a better name for your type is:
def test_something(my_module_fix: SomethingThatHasMyFuncMethod):
assert my_module_fix.my_func() = 5
So the type you probably want is something like this:
class SomethingThatHasMyFuncMethod:
def my_func(self) -> int: ...
You'll need to define it (maybe in a .pyi file). See here for info.
Finally, here's some unsolicited advice regarding:
importing the module is very expensive, so we don't want to do it on import-time
You should probably employ some method of making this module do it's thing lazily. Some of the utils in the django framework could serve as a reference point. There's also the descriptor protocol which is a bit harder to grok but may suit your needs.
You want to add type hinting for the arguments of test_something, in particular to my_module_fix. This question is hard because we can't import my_module at the top.
Well, what is the type of my_module? I'm going to assume that if you do import my_module and then type(my_module) you will get <class 'module'>.
If you're okay with not telling users exactly which module it has to be, then you could try this.
from types import ModuleType
#pytest.fixture
def my_module_fix():
import my_module
yield my_module
def test_something(my_module_fix: ModuleType):
assert my_module_fix.my_func() = 5
The downside is that a user might infer that any old module would be suitable for my_module_fix.
You want it to be more specific? There's a cost to that, but we can get around some of it (it's hard to speak on performance in VS Code vs PyCharm vs others).
In that case, let's use TYPE_CHECKING. I think this was introduced in Python 3.6.
from types import ModuleType
from typing import TYPE_CHECKING
if TYPE_CHECKING:
import my_module
#pytest.fixture
def my_module_fix():
import my_module
yield my_module
def test_something(my_module_fix: my_module):
assert my_module_fix.my_func() = 5
You won't pay these costs during normal run time. You will pay the cost of importing my_module when your type checker is doing what it does.
If you're on an earlier verison of Python, you might need to do this instead.
from types import ModuleType
from typing import TYPE_CHECKING
if TYPE_CHECKING:
import my_module
#pytest.fixture
def my_module_fix():
import my_module
yield my_module
def test_something(my_module_fix: "my_module"):
assert my_module_fix.my_func() = 5
The type checker in VS Code is smart enough to know what's going on here. I can't speak for other type checkers.
We have a Python 3.7 application that has a declared constants.py file that has this form:
APP_CONSTANT_1 = os.environ.get('app-constant-1-value')
In a test.py we were hoping to test the setting of these constants using something like this (this is highly simplified but represents the core issue):
class TestConfig:
"""General config tests"""
#pytest.fixture
def mock_os_environ(self, monkeypatch):
""" """
def mock_get(*args, **kwargs):
return 'test_config_value'
monkeypatch.setattr(os.environ, "get", mock_get)
def test_mock_env_vars(self, mock_os_environ):
import constants
assert os.environ.get('app-constant-1-value') == 'test_config_value' #passes
assert constants.APP_CONSTANT_1 == 'test_config_value' #fails
The second assertion fails as constants.constants.APP_CONSTANT_1 is None. Turns out that the constants.py seems to be loaded during pytest's 'collecting' phase and thus is already set by the time the test is run.
What are we missing here? I feel like there is a simple way to resolve this in pytest but haven't yet discovered the secret. Is there some way to avoid loading the constants file prior to the tests being run? Any ideas are appreciated.
The problem is most likely that constants has been loaded before. To make sure it gets the patched value, you have to reload it:
import os
from importlib import reload
import pytest
import constants
class TestConfig:
"""General config tests"""
#pytest.fixture
def mock_os_environ(self, monkeypatch):
""" """
monkeypatch.setenv('app-constant-1-value', 'test_config_value')
reload(constants)
def test_mock_env_vars(self, mock_os_environ):
assert os.environ.get('app-constant-1-value') == 'test_config_value'
assert app.APP_CONSTANT_1 == 'test_config_value'
Note that I used monkeypatch.setenv to specifically set the variable you need. If you don't need to change all environment variables, this is easier to use.
Erm, I would avoid using constants. You can subclass os.environment for a start, and then use a mocked subclass for your unit tests, so you can have my_env.unique_env as a member variable. You can then use eg. import json to use a json configuration file without getting involved with hard coded python.
The subclass can then hold the relevant variables (or methods if you prefer)
Being able to add a facade to os.environment provides you with the abstraction you are looking for, without any of the problems.
Even is one is using a legacy/larger project, the advantage of using an adapter for access to the environment must be apparent.
Since you are writing unit tests, there is an opportunity to use an adapter class in both the tests and the functions being tested.
We use py.test in a project and use fixtures for most test cases. But I see no possibility to use fixtures in doctest files.
To give an example with some code snippets: I have a browser fixture in conftest.py like:
#fixture
def browser(request):
from wsgi_intercept import zope_testbrowser
browser = zope_testbrowser.WSGI_Browser()
[...]
return browser
and use it in the file test_browser.txt like:
>>> browser.open('some_url')
>>> browser.url == 'some_url'
True
But I can't see a way to get the fixture into a doctest file. Is this possible at all with py.test?
It isn't supported at the moment. pytest would need to know at collection time which fixtures are going to be used in a doctest. If we can come up with a way to declare which fixtures are going to be used, it shouldn't be hard to add support to _pytest/doctest.py Maybe it's also possible to automatically find out which fixtures a doctest needs, not sure.
There's a pull request attempting to implement this over at the pytest repository. It provides two globals to doctest files, i.e. fixture_request and get_fixture (which is a convenience shortcut for fixture_request.getfuncargvalue). The intended use is:
>>> browser = get_fixture('browser')
>>> browser.open('some_url')
This is different from the .. pytest-fixtures: ... line as suggested by Holger above, but was easier to implement... :) Needless to say, it's up for discussion, of course!
Let's assume that we have a system of modules that exists only on production stage. At the moment of testing these modules do not exist. But still I would like to write tests for the code that uses those modules. Let's also assume that I know how to mock all the necessary objects from those modules. The question is: how do I conveniently add module stubs into current hierarchy?
Here is a small example. The functionality I want to test is placed in a file called actual.py:
actual.py:
def coolfunc():
from level1.level2.level3_1 import thing1
from level1.level2.level3_2 import thing2
do_something(thing1)
do_something_else(thing2)
In my test suite I already have everything I need: I have thing1_mock and thing2_mock. Also I have a testing function. What I need is to add level1.level2... into current module system. Like this:
tests.py
import sys
import actual
class SomeTestCase(TestCase):
thing1_mock = mock1()
thing2_mock = mock2()
def setUp(self):
sys.modules['level1'] = what should I do here?
#patch('level1.level2.level3_1.thing1', thing1_mock)
#patch('level1.level2.level3_1.thing1', thing2_mock)
def test_some_case(self):
actual.coolfunc()
I know that I can substitute sys.modules['level1'] with an object containing another object and so on. But it seems like a lot of code for me. I assume that there must be much simpler and prettier solution. I just cannot find it.
So, no one helped me with my problem and I decided to solve it by myself. Here is a micro-lib called surrogate which allows one to create stubs for non-existing modules.
Lib can be used with mock like this:
from surrogate import surrogate
from mock import patch
#surrogate('this.module.doesnt.exist')
#patch('this.module.doesnt.exist', whatever)
def test_something():
from this.module.doesnt import exist
do_something()
Firstly #surrogate decorator creates stubs for non-existing modules, then #patch decorator can alter them. Just as #patch, #surrogate decorators can be used "in plural", thus stubbing more than one module path. All stubs exist only at the lifetime of decorated function.
If anyone gets any use of this lib, that would be great :)