I am trying to mock the call to a function and still have the effect of the function apply. I found the solution using Python wraps, but all examples I found are applied to mocking the member method of a class. In my case, I have a pure function (not defined in a class). This seems to not work with the usual examples, as they require you to instantiate the class first I guess to obtain the real version of the method to pass into wraps.
Can this be done with pure functions?
https://wesmckinney.com/blog/spying-with-python-mocks/
My code:
<this_module.py>
def my_function:
does something important
def test_my_function:
with patch.object("this_module", "my_function", wraps="this_module.my_function")
I have an architecture, where I use wrapper for calling functions from package module. Inside the module there is a function that calls another three. I need to override one of them in run-time. Exactly I need to change parameters that are forwarded to another set of functions being called.
Here is a case sample:
a.py
import b_wrapper as wrapper
def foo():
if wrapper.bar(parameter):
"""some more code goes here"""
b_wrapper.py
import some.package.module as module
def bar(parameter):
return module.baz(veryImportantParameter, parameter)
file.py
def functionThree(par): # needs to be overwritten
"""more functions called forwarding par as a parameter"""
def baz(veryImportantParameter, parameter)
functionOne(veryImportantParameter, otherParameters)
functionTwo(veryImportantParameter, someMoreParameters)
functionThree(veryImportantParameter, parameterToChange, evenMoreParameters)
What I tried to do is overriding in wrapper file, didn't work out, as other functions are interfering with it. As reference used this post.
I'm not quite sure that this is doable, because of unique functions that are called inside this module, also looking for alternatives that won't require overriding portion of module.
Edit: mixing up arguments and parameters is intentional for demonstration purpose only.
I am quite amateur in OOP concepts of python so I wanted to know are the functionalities of self of Python in any way similar to those of this keyword of CPP/C#.
self & this have the same purpose except that self must be received explicitly.
Python is a dynamic language. So you can add members to your class. Using self explicitly let you define if you work in the local scope, instance scope or class scope.
As in C++, you can pass the instance explicitly. In the following code, #1 and #2 are actually the same. So you can use methods as normal functions with no ambiguity.
class Foo :
def call(self) :
pass
foo = Foo()
foo.call() #1
Foo.call(foo) #2
From PEP20 : Explicit is better than implicit.
Note that self is not a keyword, you can call it as you wish, it is just a convention.
Yes they implement the same concept. They serve the purpose of providing a handle to the instance of class, on which the method was executed. Or, in other wording, instance through which the method was called.
Probably someone smarter will come to point out the real differences but for a quite normal user, pythonic self is basically equivalent to c++ *this.
However the reference to self in python is used way more explicitly. E.g. it is explicitly present in method declarations. And method calls executed on the instance of object being called must be executed explicitly using self.
I.e:
def do_more_fun(self):
#haha
pass
def method1(self, other_arg):
self.do_more_fun()
This in c++ would look more like:
void do_more_fun(){
//haha
};
void method1(other_arg){
do_more_fun();
// this->do_more_fun(); // also can be called explicitly through `this`
}
Also as juanchopanza pointed out, this is a keyword in c++ so you cannot really use other name for it. This goes in pair with the other difference, you cannot omit passing this in c++ method. Only way to do it is make it static. This also holds for python but under different convention. In python 1st argument is always implicitly assigned the reference to self. So you can choose any name you like. To prevent it, and be able to make a static method in python, you need to use #staticmethod decorator (reference).
Perhaps a stupid question:
how can one specify docstring for special functions like __init__ when writing a C extension?
For ordinary methods, method table has provision for docstrings. The following autogenerated documentation is displayed when I try help(myclass):
__init__(...)
x.__init__(...) initializes x; see help(type(x)) for signature
But this is what I want to override.
I think that the most common thing to do is to just stick the definitions for the various functions into tp_doc and just leave it at that. You can then do as it says and look at your object's doc. This is what happens all over the standard library.
You don't really have any option of writing __doc__ on the various slots (tp_init, etc.) because they're wrapped by a wrapper_descriptor when you call PyType_Ready, and the docstring on a wrapper_descriptor is read-only.
I think that it is possible to skip using the slots and add your method (e.g. __init__) to your MemberDefs, but I've never tried that.
I am trying to understand, what is monkey patching or a monkey patch?
Is that something like methods/operators overloading or delegating?
Does it have anything common with these things?
No, it's not like any of those things. It's simply the dynamic replacement of attributes at runtime.
For instance, consider a class that has a method get_data. This method does an external lookup (on a database or web API, for example), and various other methods in the class call it. However, in a unit test, you don't want to depend on the external data source - so you dynamically replace the get_data method with a stub that returns some fixed data.
Because Python classes are mutable, and methods are just attributes of the class, you can do this as much as you like - and, in fact, you can even replace classes and functions in a module in exactly the same way.
But, as a commenter pointed out, use caution when monkeypatching:
If anything else besides your test logic calls get_data as well, it will also call your monkey-patched replacement rather than the original -- which can be good or bad. Just beware.
If some variable or attribute exists that also points to the get_data function by the time you replace it, this alias will not change its meaning and will continue to point to the original get_data. (Why? Python just rebinds the name get_data in your class to some other function object; other name bindings are not impacted at all.)
A MonkeyPatch is a piece of Python code which extends or modifies
other code at runtime (typically at startup).
A simple example looks like this:
from SomeOtherProduct.SomeModule import SomeClass
def speak(self):
return "ook ook eee eee eee!"
SomeClass.speak = speak
Source: MonkeyPatch page on Zope wiki.
What is a monkey patch?
Simply put, monkey patching is making changes to a module or class while the program is running.
Example in usage
There's an example of monkey-patching in the Pandas documentation:
import pandas as pd
def just_foo_cols(self):
"""Get a list of column names containing the string 'foo'
"""
return [x for x in self.columns if 'foo' in x]
pd.DataFrame.just_foo_cols = just_foo_cols # monkey-patch the DataFrame class
df = pd.DataFrame([list(range(4))], columns=["A","foo","foozball","bar"])
df.just_foo_cols()
del pd.DataFrame.just_foo_cols # you can also remove the new method
To break this down, first we import our module:
import pandas as pd
Next we create a method definition, which exists unbound and free outside the scope of any class definitions (since the distinction is fairly meaningless between a function and an unbound method, Python 3 does away with the unbound method):
def just_foo_cols(self):
"""Get a list of column names containing the string 'foo'
"""
return [x for x in self.columns if 'foo' in x]
Next we simply attach that method to the class we want to use it on:
pd.DataFrame.just_foo_cols = just_foo_cols # monkey-patch the DataFrame class
And then we can use the method on an instance of the class, and delete the method when we're done:
df = pd.DataFrame([list(range(4))], columns=["A","foo","foozball","bar"])
df.just_foo_cols()
del pd.DataFrame.just_foo_cols # you can also remove the new method
Caveat for name-mangling
If you're using name-mangling (prefixing attributes with a double-underscore, which alters the name, and which I don't recommend) you'll have to name-mangle manually if you do this. Since I don't recommend name-mangling, I will not demonstrate it here.
Testing Example
How can we use this knowledge, for example, in testing?
Say we need to simulate a data retrieval call to an outside data source that results in an error, because we want to ensure correct behavior in such a case. We can monkey patch the data structure to ensure this behavior. (So using a similar method name as suggested by Daniel Roseman:)
import datasource
def get_data(self):
'''monkey patch datasource.Structure with this to simulate error'''
raise datasource.DataRetrievalError
datasource.Structure.get_data = get_data
And when we test it for behavior that relies on this method raising an error, if correctly implemented, we'll get that behavior in the test results.
Just doing the above will alter the Structure object for the life of the process, so you'll want to use setups and teardowns in your unittests to avoid doing that, e.g.:
def setUp(self):
# retain a pointer to the actual real method:
self.real_get_data = datasource.Structure.get_data
# monkey patch it:
datasource.Structure.get_data = get_data
def tearDown(self):
# give the real method back to the Structure object:
datasource.Structure.get_data = self.real_get_data
(While the above is fine, it would probably be a better idea to use the mock library to patch the code. mock's patch decorator would be less error prone than doing the above, which would require more lines of code and thus more opportunities to introduce errors. I have yet to review the code in mock but I imagine it uses monkey-patching in a similar way.)
According to Wikipedia:
In Python, the term monkey patch only
refers to dynamic modifications of a
class or module at runtime, motivated
by the intent to patch existing
third-party code as a workaround to a
bug or feature which does not act as
you desire.
First: monkey patching is an evil hack (in my opinion).
It is often used to replace a method on the module or class level with a custom implementation.
The most common usecase is adding a workaround for a bug in a module or class when you can't replace the original code. In this case you replace the "wrong" code through monkey patching with an implementation inside your own module/package.
Monkey patching can only be done in dynamic languages, of which python is a good example. Changing a method at runtime instead of updating the object definition is one example;similarly, adding attributes (whether methods or variables) at runtime is considered monkey patching. These are often done when working with modules you don't have the source for, such that the object definitions can't be easily changed.
This is considered bad because it means that an object's definition does not completely or accurately describe how it actually behaves.
Monkey patching is reopening the existing classes or methods in class at runtime and changing the behavior, which should be used cautiously, or you should use it only when you really need to.
As Python is a dynamic programming language, Classes are mutable so you can reopen them and modify or even replace them.
What is monkey patching? Monkey patching is a technique used to dynamically update the behavior of a piece of code at run-time.
Why use monkey patching? It allows us to modify or extend the behavior of libraries, modules, classes or methods at runtime without
actually modifying the source code
Conclusion Monkey patching is a cool technique and now we have learned how to do that in Python. However, as we discussed, it has its
own drawbacks and should be used carefully.