I'm working on code that retrieves information from Twilio's Flow system through their API. That part of the code functions fine, but when I try to mock it for unit testing, it's throwing an error from the mocked api response.
Here is the code being tested:
from twilio.rest import Client
class FlowChecker:
def __init__(self, twilio_sid, twilio_auth_token):
self.twilio_SID = twilio_sid
self.twilio_auth_token = twilio_auth_token
self.client = Client(self.twilio_SID, self.twilio_auth_token)
self.calls = self.client.calls.list()
self.flows = self.client.studio.v2.flows
def get_active_executions(self):
active_executions = []
for flow in self.flows.list():
executions = self.client.studio.v2.flows(flow.sid).executions.list()
for execution in executions:
if execution._properties['status'] != 'ended':
active_executions.append({'flow_sid': flow.sid, 'execution': execution})
And here is my unit test code that's throwing the error:
import unittest
from unittest.mock import Mock, patch
from flows.twilio_flows import FlowChecker
class FlowCheckerTest(unittest.TestCase):
#patch('flows.twilio_flows.Client')
def test_get_active_flows(self, mock_client):
flow_checker = FlowChecker('fake_sid', 'fake_auth_token')
mock_call = Mock()
mock_flow = Mock()
mock_flow.sid = 0
mock_execution = Mock()
mock_execution._properties = {'status': 'ended'}
mock_client.calls.list().return_value = [mock_call]
mock_client.studio.v2.flows = [mock_flow]
mock_client.studio.v2.flows(mock_flow.sid).executions.list().return_value = [mock_execution]
self.assertEqual(flow_checker.get_active_executions(), [])
And here is the error traceback:
Ran 2 tests in 0.045s
FAILED (errors=1)
Error
Traceback (most recent call last):
File "C:\Users\Devon\AppData\Local\Programs\Python\Python310\lib\unittest\mock.py", line 1369, in patched
return func(*newargs, **newkeywargs)
File "C:\Users\Devon\PycharmProjects\Day_35\tests\twilio_flows_test'.py", line 19, in test_get_active_flows_when_empty
mock_client.studio.v2.flows(mock_flow.sid).executions.list().return_value = [mock_execution]
TypeError: 'list' object is not callable
Process finished with exit code 1
As you can see, "mock_client.calls.list().return_value = [mock_call]" doesn't throw any errors during init, and the first code block runs fine. It's only the mocked executions.list() that's throwing the error in the test.
Can anyone clear this up?
Thank you!
I've tried researching this specific issue and was unable to find information addressing it. It's a very specific deeply nested function in a vendor supplied client that I need to test, so I don't know what to try.
The problem isn't with .list(), it's with .flows().
mock_client.studio.v2.flows = [mock_flow]
mock_client.studio.v2.flows(mock_flow.sid).executions.list().return_value = [mock_execution]
You assign .flows to be a list, and then you try to call it like a function, which causes the error.
I think maybe you intended to say .flows[mock_flow.sid] instead of .flows(mock_flow.sid)?
Although even that doesn't make sense. .flows is a one-element list, so you would use .flows[0] to access the first (and only) item.
As my MRE, I've got the following file:
blah.py
'''Blah module'''
import pydantic
class Foo:
'''Foo class'''
class Bar(pydantic.BaseModel):
'''Bar class'''
x: str = pydantic.Field(description='The x.')
#pydantic.validator('x')
def do_nothing(cls, value: str) -> str:
return value
I'm attempting to use Sphinx to generate documentation for this module. In my conf.py, I have
extensions = [
'sphinx.ext.autodoc',
'sphinxcontrib.autodoc_pydantic',
]
My blah.rst is
Blah
====
.. automodule:: blah.blah
:members:
I've pip installed pydantic and autodoc_pydantic.
However, when I make html, I get
Exception occurred:
File "/home/user/Projects/Workspace/env/lib/python3.10/site-packages/sphinxcontrib/autodoc_pydantic/inspection.py", line 311, in __init__
self.attribute: Dict = self.model.Config
AttributeError: type object 'Foo' has no attribute 'Config'
It appears that autodoc_pydantic thinks that Foo inherits from pydantic.BaseModel when it's really Bar that does. If I remove 'sphinxcontrib.autodoc_pydantic' from extensions, the error goes away.
More interestingly, if I delete the validator, the error goes away as well.
autodoc_pydantic is version 1.6.1.
This issue was fixed in version 1.7.2.
I have a method in Python that looks like this (in comicfile.py):
from zipfile import ZipFile
...
class ComicFile():
...
def page_count(self):
"""Return the number of pages in the file."""
if self.file == None:
raise ComicFile.FileNoneError()
if not os.path.isfile(self.file):
raise ComicFile.FileNotFoundError()
with ZipFile(self.file) as zip:
members = zip.namelist()
pruned = self.prune_dirs(members)
length = len(pruned)
return length
I'm trying to write a unit test for this (I've already tested prune_dirs), and so for this is what I have (test_comicfile.py):
import unittest
import unittest.mock
import comicfile
...
class TestPageCount(unittest.TestCase):
def setUp(self):
self.comic_file = comicfile.ComicFile()
#unittest.mock.patch('comicfile.ZipFile')
def test_page_count(self, mock_zip_file):
# Store as tuples to use as dictionary keys.
members_dict = {('dir/', 'dir/file1', 'dir/file2'):2,
('file1.jpg', 'file2.jpg', 'file3.jpg'):3
}
# Make the file point to something to prevent FileNoneError.
self.comic_file.file = __file__
for file_tuple, count in members_dict.items():
mock_zip_file.return_value.namelist = list(file_tuple)
self.assertEqual(count, self.comic_file.page_count())
When I run this test, I get the following:
F..ss....
======================================================================
FAIL: test_page_count (test_comicfile.TestPageCount)
----------------------------------------------------------------------
Traceback (most recent call last):
File "/usr/local/Cellar/python3/3.5.1/Frameworks/Python.framework/Versions/3.5/lib/python3.5/unittest/mock.py", line 1157, in patched
return func(*args, **keywargs)
File "/Users/chuck/Dropbox/Projects/chiv/chiv.cbstar/test_comicfile.py", line 86, in test_page_count
self.assertEqual(count, self.comic_file.page_count())
AssertionError: 2 != 0
----------------------------------------------------------------------
Ran 9 tests in 0.010s
FAILED (failures=1, skipped=2)
OK, so self.comic_file.page_count() is returning 0. I tried placing the following line after members = zip.namelist() in page_count.
print('\nmembers -> ' + str(members))
During the test, I get this:
members -> <MagicMock name='ZipFile().__enter__().namelist()' id='4483358280'>
I'm quite new to unit testing and am quite nebulous on using unittest.mock, but my understanding is that mock_zip-file.return_value.namelist = list(file_tuple) should have made it so that the namelist method of the ZipFile class would return each of the file_tuple contents in turn. What it is doing I have no idea.
I think what I'm trying to do here is clear, but I can't seem to figure out how to override the namelist method so that my unit test is only testing this one function instead of having to deal with ZipFile as well.
ZipFile is instantiated as a context manager. to mock it you have to refer to its __enter__ method.
mock_zip_file.return_value.__enter__.return_value.namelist.return_value = list(file_tuple)
What you're trying to do is very clear, but the context manager adds complexity to the mocking.
One trick is that when a mock registers all calls made to it, in this example it is saying it has a call at:
members -> <MagicMock name='ZipFile().__enter__().namelist()' id='4483358280'>
This can guide you in registering your mocked object, replace all () with return_value
I'm writing a piece of software over on github. It's basically a tray icon with some extra features. I want to provide a working piece of code without actually having to make the user install what are essentially dependencies for optional features and I don't actually want to import things I'm not going to use so I thought code like this would be "good solution":
---- IN LOADING FUNCTION ----
features = []
for path in sys.path:
if os.path.exists(os.path.join(path, 'pynotify')):
features.append('pynotify')
if os.path.exists(os.path.join(path, 'gnomekeyring.so')):
features.append('gnome-keyring')
#user dialog to ask for stuff
#notifications available, do you want them enabled?
dlg = ConfigDialog(features)
if not dlg.get_notifications():
features.remove('pynotify')
service_start(features ...)
---- SOMEWHERE ELSE ------
def service_start(features, other_config):
if 'pynotify' in features:
import pynotify
#use pynotify...
There are some issues however. If a user formats his machine and installs the newest version of his OS and redeploys this application, features suddenly disappear without warning. The solution is to present this on the configuration window:
if 'pynotify' in features:
#gtk checkbox
else:
#gtk label reading "Get pynotify and enjoy notification pop ups!"
But if this is say, a mac, how do I know I'm not sending the user on a wild goose chase looking for a dependency they can never fill?
The second problem is the:
if os.path.exists(os.path.join(path, 'gnomekeyring.so')):
issue. Can I be sure that the file is always called gnomekeyring.so across all the linux distros?
How do other people test these features? The problem with the basic
try:
import pynotify
except:
pynotify = disabled
is that the code is global, these might be littered around and even if the user doesn't want pynotify....it's loaded anyway.
So what do people think is the best way to solve this problem?
The try: method does not need to be global — it can be used in any scope and so modules can be "lazy-loaded" at runtime. For example:
def foo():
try:
import external_module
except ImportError:
external_module = None
if external_module:
external_module.some_whizzy_feature()
else:
print("You could be using a whizzy feature right now, if you had external_module.")
When your script is run, no attempt will be made to load external_module. The first time foo() is called, external_module is (if available) loaded and inserted into the function's local scope. Subsequent calls to foo() reinsert external_module into its scope without needing to reload the module.
In general, it's best to let Python handle import logic — it's been doing it for a while. :-)
You might want to have a look at the imp module, which basically does what you do manually above. So you can first look for a module with find_module() and then load it via load_module() or by simply importing it (after checking the config).
And btw, if using except: I always would add the specific exception to it (here ImportError) to not accidently catch unrelated errors.
Not sure if this is good practice, but I created a function that does the optional import (using importlib) and error handling:
def _optional_import(module: str, name: str = None, package: str = None):
import importlib
try:
module = importlib.import_module(module)
return module if name is None else getattr(module, name)
except ImportError as e:
if package is None:
package = module
msg = f"install the '{package}' package to make use of this feature"
raise ValueError(msg) from e
If an optional module is not available, the user will at least get the idea what to do. E.g.
# code ...
if file.endswith('.json'):
from json import load
elif file.endswith('.yaml'):
# equivalent to 'from yaml import safe_load as load'
load = _optional_import('yaml', 'safe_load', package='pyyaml')
# code using load ...
The main disadvantage with this approach is that your imports have to be done in-line and are not all on the top of your file. Therefore, it might be considered better practice to use a slight adaptation of this function (assuming that you are importing a function or the like):
def _optional_import_(module: str, name: str = None, package: str = None):
import importlib
try:
module = importlib.import_module(module)
return module if name is None else getattr(module, name)
except ImportError as e:
if package is None:
package = module
msg = f"install the '{package}' package to make use of this feature"
import_error = e
def _failed_import(*args):
raise ValueError(msg) from import_error
return _failed_import
Now, you can make the imports with the rest of your imports and the error will only be raised when the function that failed to import is actually used. E.g.
from utils import _optional_import_ # let's assume we import the function
from json import load as json_load
yaml_load = _optional_import_('yaml', 'safe_load', package='pyyaml')
# unimportant code ...
with open('test.txt', 'r') as fp:
result = yaml_load(fp) # will raise a value error if import was not successful
PS: sorry for the late answer!
Another option is to use #contextmanager and with. In this situation, you do not know beforehand which dependencies are needed:
from contextlib import contextmanager
#contextmanager
def optional_dependencies(error: str = "ignore"):
assert error in {"raise", "warn", "ignore"}
try:
yield None
except ImportError as e:
if error == "raise":
raise e
if error == "warn":
msg = f'Missing optional dependency "{e.name}". Use pip or conda to install.'
print(f'Warning: {msg}')
Usage:
with optional_dependencies("warn"):
import module_which_does_not_exist_1
import module_which_does_not_exist_2
z = 1
print(z)
Output:
Warning: Missing optional dependency "module_which_does_not_exist_1". Use pip or conda to install.
---------------------------------------------------------------------------
NameError Traceback (most recent call last)
Cell In [43], line 5
3 import module_which_does_not_exist_2
4 z = 1
----> 5 print(z)
NameError: name 'z' is not defined
Here, you should define all your imports immediately after with. The first module which is not installed will throw ImportError, which is caught by optional_dependencies. Depending on how you want to handle this error, it will either ignore it, print a warning, or raise it again.
The entire code will only run if all the modules are installed.
Here's a production-grade solution, using importlib and Pandas's import_optional_dependency as suggested by #dre-hh
from typing import *
import importlib, types
def module_exists(
*names: Union[List[str], str],
error: str = "ignore",
warn_every_time: bool = False,
__INSTALLED_OPTIONAL_MODULES: Dict[str, bool] = {}
) -> Optional[Union[Tuple[types.ModuleType, ...], types.ModuleType]]:
"""
Try to import optional dependencies.
Ref: https://stackoverflow.com/a/73838546/4900327
Parameters
----------
names: str or list of strings.
The module name(s) to import.
error: str {'raise', 'warn', 'ignore'}
What to do when a dependency is not found.
* raise : Raise an ImportError.
* warn: print a warning.
* ignore: If any module is not installed, return None, otherwise,
return the module(s).
warn_every_time: bool
Whether to warn every time an import is tried. Only applies when error="warn".
Setting this to True will result in multiple warnings if you try to
import the same library multiple times.
Returns
-------
maybe_module : Optional[ModuleType, Tuple[ModuleType...]]
The imported module(s), if all are found.
None is returned if any module is not found and `error!="raise"`.
"""
assert error in {"raise", "warn", "ignore"}
if isinstance(names, (list, tuple, set)):
names: List[str] = list(names)
else:
assert isinstance(names, str)
names: List[str] = [names]
modules = []
for name in names:
try:
module = importlib.import_module(name)
modules.append(module)
__INSTALLED_OPTIONAL_MODULES[name] = True
except ImportError:
modules.append(None)
def error_msg(missing: Union[str, List[str]]):
if not isinstance(missing, (list, tuple)):
missing = [missing]
missing_str: str = ' '.join([f'"{name}"' for name in missing])
dep_str = 'dependencies'
if len(missing) == 1:
dep_str = 'dependency'
msg = f'Missing optional {dep_str} {missing_str}. Use pip or conda to install.'
return msg
missing_modules: List[str] = [name for name, module in zip(names, modules) if module is None]
if len(missing_modules) > 0:
if error == "raise":
raise ImportError(error_msg(missing_modules))
if error == "warn":
for name in missing_modules:
## Ensures warning is printed only once
if warn_every_time is True or name not in __INSTALLED_OPTIONAL_MODULES:
print(f'Warning: {error_msg(name)}')
__INSTALLED_OPTIONAL_MODULES[name] = False
return None
if len(modules) == 1:
return modules[0]
return tuple(modules)
Usage: ignore errors (error="ignore", default behavior)
Suppose we want to run certain code only if the required libraries exists:
if module_exists("pydantic", "sklearn"):
from pydantic import BaseModel
from sklearn.metrics import accuracy_score
class AccuracyCalculator(BaseModel):
num_decimals: int = 5
def calculate(self, y_pred: List, y_true: List) -> float:
return round(accuracy_score(y_true, y_pred), self.num_decimals)
print("Defined AccuracyCalculator in global context")
If either dependencies pydantic or skelarn do not exist, then the class AccuracyCalculator will not be defined and the print statement will not run.
Usage: raise ImportError (error="raise")
Alternatively, you can raise a error if any module does not exist:
if module_exists("pydantic", "sklearn", error="raise"):
from pydantic import BaseModel
from sklearn.metrics import accuracy_score
class AccuracyCalculator(BaseModel):
num_decimals: int = 5
def calculate(self, y_pred: List, y_true: List) -> float:
return round(accuracy_score(y_true, y_pred), self.num_decimals)
print("Defined AccuracyCalculator in global context")
Output:
line 60, in module_exists(error, __INSTALLED_OPTIONAL_MODULES, *names)
58 if len(missing_modules) > 0:
59 if error == "raise":
---> 60 raise ImportError(error_msg(missing_modules))
61 if error == "warn":
62 for name in missing_modules:
ImportError: Missing optional dependencies "pydantic" "sklearn". Use pip or conda to install.
Usage: print a warning (error="warn")
Alternatively, you can print a warning if the module does not exist.
if module_exists("pydantic", "sklearn", error="warn"):
from pydantic import BaseModel
from sklearn.metrics import accuracy_score
class AccuracyCalculator(BaseModel):
num_decimals: int = 5
def calculate(self, y_pred: List, y_true: List) -> float:
return round(accuracy_score(y_true, y_pred), self.num_decimals)
print("Defined AccuracyCalculator in global context")
if module_exists("pydantic", "sklearn", error="warn"):
from pydantic import BaseModel
from sklearn.metrics import roc_auc_score
class RocAucCalculator(BaseModel):
num_decimals: int = 5
def calculate(self, y_pred: List, y_true: List) -> float:
return round(roc_auc_score(y_true, y_pred), self.num_decimals)
print("Defined RocAucCalculator in global context")
Output:
Warning: Missing optional dependency "pydantic". Use pip or conda to install.
Warning: Missing optional dependency "sklearn". Use pip or conda to install.
Here, we ensure that only one warning is printed for each missing module, otherwise you would get a warning each time you try to import.
This is very useful for Python libraries where you might try to import the same optional dependencies many times, and only want to see one Warning.
You can pass warn_every_time=True to always print the warning when you try to import.
I'm really excited to share this new technique I came up with to handle optional dependencies!
The concept is to produce the error when the uninstalled package is used not imported.
Just add a single call before your imports. You don't need to change any code at all. No more using try: when importing. No more using conditional skip decorators when writing tests.
Main components
An importer to return a fake module for missing imports
A fake module that raises an exception when it's used
A custom Exception that will skip tests automatically if raised within one
Minimal Code Example
import sys
import importlib
from unittest.case import SkipTest
from _pytest.outcomes import Skipped
class MissingOptionalDependency(SkipTest, Skipped):
def __init__(self, msg=None):
self.msg = msg
def __repr__(self):
return f"MissingOptionalDependency: {self.msg}" if self.msg else f"MissingOptionalDependency"
class GeneralImporter:
def __init__(self, *names):
self.names = names
sys.meta_path.insert(0, self)
def find_spec(self, fullname, path=None, target=None):
if fullname in self.names:
return importlib.util.spec_from_loader(fullname, self)
def create_module(self, spec):
return FakeModule(name=spec.name)
def exec_module(self, module):
pass
class FakeModule:
def __init__(self, name):
self.name = name
def __call__(self, *args, **kwargs):
raise MissingOptionalDependency(f"Optional dependency '{self.name}' was used but it isn't installed.")
GeneralImporter("notinstalled")
import notinstalled # No error
print(notinstalled) # <__main__.FakeModule object at 0x0000014B7F6D9E80>
notinstalled() # MissingOptionalDependency: Optional dependency 'notinstalled' was used but it isn't installed.
Package
The technique above has some shortcomings that my package fixes.
It's open-source, lightweight, and has no dependencies!
Some key differences to the example above:
Covers more than 100 dunder methods (All tested)
Covers 15 common dunder attribute lookups
Entry function is generalimport which returns an ImportCatcher
ImportCatcher holds names, scope, and caught names
It can be enabled and disabled
The scope prevents external packages from being affected
Wildcard support to allow any package to be imported
Puts the importer first in sys.meta_path
Lets it catch namespace imports (Usually occurs with uninstalled packages)
Generalimport on GitHub
pip install generalimport
Minimal example
from generalimport import generalimport
generalimport("notinstalled")
from notinstalled import missing_func # No error
missing_func() # Error occurs here
The readme on GitHub goes more in-depth
One way to handle the problem of different dependencies for different features is to implement the optional features as plugins. That way the user has control over which features are activated in the app but isn't responsible for tracking down the dependencies herself. That task then gets handled at the time of each plugin's installation.
I'm having difficuly getting my head around python's mock test methodology.
I want to do some mocking on this file.
Since packages xbmc, xbmcaddon and xbmcgui cannot be imported in a normal python environment I've managed to mock them out like this:
class XBMCTestCase(unittest.TestCase):
def setUp(self):
#Mock up any calls to modules that cannot be imported
self.xbmc = Mock()
self.xbmcgui = Mock()
self.xbmcaddon = Mock()
modules = {
'xbmc' : self.xbmc,
'xbmcgui': self.xbmcgui,
'xbmcaddon': self.xbmcaddon
}
self.module_patcher = patch.dict('sys.modules', modules) ##UndefinedVariable
self.module_patcher.start()
See it in action here.
So when I import setlocation.py I get an error like this:
File "/home/paulo/workspace/weather.metoffice/src/metoffice/utils/utilities.py", line 21, in <module>
CACHE_FOLDER = os.path.join(ADDON_DATA_PATH, 'cache')
File "/usr/lib/python2.7/posixpath.py", line 78, in join
path += b
TypeError: unsupported operand type(s) for +=: 'Mock' and 'str'
Even if I mock out 'metoffice.utils' (by adding it to the list of modules in the patch created at setup) I get a similar error in setlocation.py
File "/home/paulo/workspace/weather.metoffice/src/metoffice/setlocation.py", line 32, in <module>
GEOIP_PROVIDER = int(__addon__.getSetting('GeoIPProvider'))
TypeError: int() argument must be a string or a number, not 'Mock'
So I need __addon__.getSetting() to return a string.
Any ideas?
All attempts have failed, but I don't think I have a full grasp of the capabilities of the mock package.
Note I'm on Python 2.7.3 with mock 1.0.1
You need to tell your mocks what to return. The __addon__ value is the result of a xbmcaddon.Addon() call, so you can get access to that mock object with:
addon = self.xbmcaddon.Addon.return_value
because .return_value gives you the actual Mock object that calling Addon() would return.
Now you can tell that Mock object what to return when the getSetting() method is called; there are two values to provide here, so you could use the side_effect to set a sequence of values to return:
addon.getSetting.side_effect = ['some_api_key', '42']
where the first call to __addon__.getSetting() will produce the first value 'some_api_key', the second cal will produce '42'.