I'm using Mock (http://www.voidspace.org.uk/python/mock/mock.html), and came across a particular mock case that I cant figure out the solution.
I have a function with multiple calls to some_function that is being Mocked.
def function():
some_function(1)
some_function(2)
some_function(3)
I only wanna mock the first and third call to some_function. The second call I wanna to be made to the real some_function.
I tried some alternatives with http://www.voidspace.org.uk/python/mock/mock.html#mock.Mock.mock_calls, but with no success.
Thanks in advance for the help.
It seems that the wraps argument could be what you want:
wraps: Item for the mock object to wrap. If wraps is not None then calling the
Mock will pass the call through to the wrapped object (returning the
real result and ignoring return_value).
However, since you only want the second call to not be mocked, I would suggest the use of mock.side_effect.
If side_effect is an iterable then each call to the mock will return
the next value from the iterable.
If you want to return a different value for each call, it's a perfect fit :
somefunction_mock.side_effect = [10, None, 10]
Only the first and third calls to somefunction will return 10.
However, if you do need to call the real function, but not the second time, you can also pass side_effect a callable, but I find it pretty ugly (there might be a smarter to do it):
class CustomMock(object):
calls = 0
def some_function(self, arg):
self.calls += 1
if self.calls != 2:
return my_real_function(arg)
else:
return DEFAULT
somefunction_mock.side_effect = CustomMock().some_function
Even simpler than creating a CustomMock class :
def side_effect(*args, **kwargs):
if side_effect.counter < 10:
side_effect.counter += 1
return my_real_function(arg)
else:
return DEFAULT
side_effect.counter = 0
mocked_method.side_effect = side_effect
I faced the same situation today. After some hesitation I found a different way to work around it.
At first, I have a function looks like this:
def reboot_and_balabala(args):
os.system('do some prepare here')
os.system('reboot')
sys.exit(0)
I want the first call to os.system be invoked, otherwise the local file is not generated, and I cannot verify it.
But I really do not want the second call to os.system be invoked, lol.
At first, I have an unittest similar to:
def test_reboot_and_balabala(self):
with patch.object(os, 'system') as mock_system:
# do some mock setup on mock_system, this is what I looked for
# but I do not found any easy and clear solution
with patch.object(sys, 'exit') as mock_exit:
my_lib.reboot_and_balabala(...)
# assert mock invoke args
# check generated files
But finally, I realized, after adjusting the code, I have a more better code structure, and unittests, by following way:
def reboot():
os.system('reboot')
sys.exit(0)
def reboot_and_balabala(args):
os.system('do some prepare here')
reboot()
And then we can test those code by:
def test_reboot(self):
with patch.object(os, 'system') as mock_system:
with patch.object(sys, 'exit') as mock_exit:
my_lib.reboot()
mock_system.assert_called_once_with('reboot')
mock_exit.assert_called_once_with(0)
def test_reboot_and_balabala(self):
with patch.object(my_lib, 'reboot') as mock_reboot:
my_lib.reboot_and_balabala(...)
# check generated files here
mock_reboot.assert_called_once()
This is not a direct answer to the question. But I think this is very inspiring.
Related
I would like a way to limit the calling of a function to once per values of parameters.
For example
def unique_func(x):
return x
>>> unique_func([1])
[1]
>>> unique_func([1])
*** wont return anything ***
>>> unique_func([2])
[2]
Any suggestions? I've looked into using memoization but not established a solution just yet.
This is not solved by the suggested Prevent a function from being called twice in a row since that only solves when the previous func call had them parameters.
Memoization uses a mapping of arguments to return values. Here, you just want a mapping of arguments to None, which can be handled with a simple set.
def idempotize(f):
cache = set()
def _(x):
if x in cache:
return
cache.add(x)
return f(x)
return _
#idempotize
def unique_fun(x):
...
With some care, this can be generalized to handle functions with multiple arguments, as long as they are hashable.
def idempotize(f):
cache = set()
def _(*args, **kwargs):
k = (args, frozenset(kwargs.items()))
if k in cache:
return
return f(*args, **kwargs)
return _
Consider using the built-in functools.lru_cache() instead of rolling your own.
It won't return nothing on the second function call with the same arugments (it will return the same thing as the first function call) but maybe you can live with that. It would seem like a negligible price to pay, compared to the advantages of using something that's maintained as part of the standard library.
Requires your argument x to be hashable, so won't work with lists. Strings are fine.
from functools import lru_cache
#lru_cache()
def unique_fun(x):
...
I've built a function decorator to handle this scenario, that limits function calls to the same function in a given timeframe.
You can directly use it via PyPI with pip install ofunctions.threading or checkout the github sources.
Example: I want to limit calls to the same function with the same parameters to one call per 10 seconds:
from ofunctions.threading import no_flood
#no_flood(10)
def my_function():
print("It's me, the function")
for _ in range(0, 5):
my_function()
# Will print the text only once.
if after 10 seconds the function is called again, we'll allow a new execution, but will prevent any other execution for the next 10 seconds.
By default #no_flood will limit function calls with the same parameter, so that calling func(1) and func(2) are still allowed concurrently.
The #no_flood decorator can also limit all function calls to a given function regardless of it's parameters:
from ofunctions.threading import no_flood
#no_flood(10, False)
def my_function(var):
print("It's me, function number {}".format(var))
for i in range(0, 5):
my_function(i)
# Will only print function text once
Need help, I am trying to add a change the functionality of c command to quit command, these changes are needed for further creation of new commands. I don't what I am doing wrong, how these two things are different first one is working fine but second one is not , I am just changing the behaviour
db = pdb.Pdb()
db.do_c = db.do_quit
no = 3
db.runcall(fun,no)
But this is not working , in this case self.do_quit is not getting even called.
class dbg(pdb.Pdb):
def custom_quit(self,arg):
self.do_quit
db = dbg()
no = 3
db.do_c = db.custom_quit
db.runcall(fun,no)
I am just running on simple function fun
def fun(no):
print("a")
print("b")
for i in range(0,no):
print(i)
return 'abc'
on command c it does nothing.
The usual way to extend a method in a class is to use the same name
for the method (that is override it) while calling super() to preserve
original method functionality:
So you can change your custom method to
class dbg(pdb.Pdb):
def do_quit(self, arg):
super().do_quit(arg)
print('do something else')
return(1)
and monkey patch it with:
db.do_c = db.do_quit # do_quit as usual
Take a look into pdb.py and search for the do_quit function and you'll
understand something is done that you have to do, or somehow preserve,
including return(1)
I have encountered a problem when I write a unit test. This is a chunck from an unit test file:
main.obj = MainObj.objects.create(short_url="a1b2c3")
with unittest.mock.patch('prj.apps.app.models.base.generate_url_string', return_value="a1b2c3") as mocked_generate_url_string:
obj.generate_short_url()
This is a chunk of code from the file 'prj.apps.app.models.base' (file which imports function 'generate_url_string' which is being mocked):
from ..utils import generate_url_string
.....................
def generate_short_url(self):
short_url = generate_url_string()
while MainObj.objects.filter(short_url=short_url).count():
short_url = generate_url_string()
return short_url
I want to show in the unit test that the function 'generate_short_url' doesn't return repeated values if some objects in the system have similar short_urls. I mocked 'generate_url_string' with predefined return result for this purpose.
The problem is that I couldn't limit number of calls of mocked function with this value, and as a result the code goes to an infinite loop.
I would like to call my function with predefined result ('a1b2c3') only once. After that I want function to work as usual. Something like this:
with unittest.mock.patch('prj.apps.app.models.base.generate_url_string', return_value="a1b2c3", times_to_call=1) as mocked_generate_url_string:
obj.generate_short_url()
But I see no any attributes like 'times_to_call' in a mocking library.
Is there any way to handle that ?
Define a generator that first yields the fixed value, then yields the return value of the real function (which is passed as an argument to avoid calling the patched value).
def mocked(x):
yield "a1b2c3"
while True:
yield x()
Then, use the generator as the side effect of the patched function.
with unittest.mock.patch(
'prj.apps.app.models.base.generate_url_string',
side_effect=mocked(prj.apps.app.models.base.generate_url_string)) as mocked_generate_url_string:
obj.generate_short_url()
I often do interactive work in Python that involves some expensive operations that I don't want to repeat often. I'm generally running whatever Python file I'm working on frequently.
If I write:
import functools32
#functools32.lru_cache()
def square(x):
print "Squaring", x
return x*x
I get this behavior:
>>> square(10)
Squaring 10
100
>>> square(10)
100
>>> runfile(...)
>>> square(10)
Squaring 10
100
That is, rerunning the file clears the cache. This works:
try:
safe_square
except NameError:
#functools32.lru_cache()
def safe_square(x):
print "Squaring", x
return x*x
but when the function is long it feels strange to have its definition inside a try block. I can do this instead:
def _square(x):
print "Squaring", x
return x*x
try:
safe_square_2
except NameError:
safe_square_2 = functools32.lru_cache()(_square)
but it feels pretty contrived (for example, in calling the decorator without an '#' sign)
Is there a simple way to handle this, something like:
#non_resetting_lru_cache()
def square(x):
print "Squaring", x
return x*x
?
Writing a script to be executed repeatedly in the same session is an odd thing to do.
I can see why you'd want to do it, but it's still odd, and I don't think it's unreasonable for the code to expose that oddness by looking a little odd, and having a comment explaining it.
However, you've made things uglier than necessary.
First, you can just do this:
#functools32.lru_cache()
def _square(x):
print "Squaring", x
return x*x
try:
safe_square_2
except NameError:
safe_square_2 = _square
There is no harm in attaching a cache to the new _square definition. It won't waste any time, or more than a few bytes of storage, and, most importantly, it won't affect the cache on the previous _square definition. That's the whole point of closures.
There is a potential problem here with recursive functions. It's already inherent in the way you're working, and the cache doesn't add to it in any way, but you might only notice it because of the cache, so I'll explain it and show how to fix it. Consider this function:
#lru_cache()
def _fact(n):
if n < 2:
return 1
return _fact(n-1) * n
When you re-exec the script, even if you have a reference to the old _fact, it's going to end up calling the new _fact, because it's accessing _fact as a global name. It has nothing to do with the #lru_cache; remove that, and the old function will still end up calling the new _fact.
But if you're using the renaming trick above, you can just call the renamed version:
#lru_cache()
def _fact(n):
if n < 2:
return 1
return fact(n-1) * n
Now the old _fact will call fact, which is still the old _fact. Again, this works identically with or without the cache decorator.
Beyond that initial trick, you can factor that whole pattern out into a simple decorator. I'll explain step by step below, or see this blog post.
Anyway, even with the less-ugly version, it's still a bit ugly and verbose. And if you're doing this dozens of times, my "well, it should look a bit ugly" justification will wear thin pretty fast. So, you'll want to handle this the same way you always factor out ugliness: wrap it in a function.
You can't really pass names around as objects in Python. And you don't want to use a hideous frame hack just to deal with this. So you'll have to pass the names around as strings. ike this:
globals().setdefault('fact', _fact)
The globals function just returns the current scope's global dictionary. Which is a dict, which means it has the setdefault method, which means this will set the global name fact to the value _fact if it didn't already have a value, but do nothing if it did. Which is exactly what you wanted. (You could also use setattr on the current module, but I think this way emphasizes that the script is meant to be (repeatedly) executed in someone else's scope, not used as a module.)
So, here that is wrapped up in a function:
def new_bind(name, value):
globals().setdefault(name, value)
… which you can turn that into a decorator almost trivially:
def new_bind(name):
def wrap(func):
globals().setdefault(name, func)
return func
return wrap
Which you can use like this:
#new_bind('foo')
def _foo():
print(1)
But wait, there's more! The func that new_bind gets is going to have a __name__, right? If you stick to a naming convention, like that the "private" name must be the "public" name with a _ prefixed, we can do this:
def new_bind(func):
assert func.__name__[0] == '_'
globals().setdefault(func.__name__[1:], func)
return func
And you can see where this is going:
#new_bind
#lru_cache()
def _square(x):
print "Squaring", x
return x*x
There is one minor problem: if you use any other decorators that don't wrap the function properly, they will break your naming convention. So… just don't do that. :)
And I think this works exactly the way you want in every edge case. In particular, if you've edited the source and want to force the new definition with a new cache, you just del square before rerunning the file, and it works.
And of course if you want to merge those two decorators into one, it's trivial to do so, and call it non_resetting_lru_cache.
However, I'd keep them separate. I think it's more obvious what they do. And if you ever want to wrap another decorator around #lru_cache, you're probably still going to want #new_bind to be the outermost decorator, right?
What if you want to put new_bind into a module that you can import? Then it's not going to work, because it will be referring to the globals of that module, not the one you're currently writing.
You can fix that by explicitly passing your globals dict, or your module object, or your module name as an argument, like #new_bind(__name__), so it can find your globals instead of its. But that's ugly and repetitive.
You can also fix it with an ugly frame hack. At least in CPython, sys._getframe() can be used to get your caller's frame, and frame objects have a reference to their globals namespace, so:
def new_bind(func):
assert func.__name__[0] == '_'
g = sys._getframe(1).f_globals
g.setdefault(func.__name__[1:], func)
return func
Notice the big box in the docs that tells you this is an "implementation detail" that may only apply to CPython and is "for internal and specialized purposes only". Take this seriously. Whenever someone has a cool idea for the stdlib or builtins that could be implemented in pure Python, but only by using _getframe, it's generally treated almost the same as an idea that can't be implemented in pure Python at all. But if you know what you're doing, and you want to use this, and you only care about present-day versions of CPython, it will work.
There is no persistent_lru_cache in the stdlib. But you can build one pretty easily.
The functools source is linked directly from the docs, because this is one of those modules that's as useful as sample code as it is for using it directly.
As you can see, the cache is just a dict. If you replace that with, say, a shelf, it will become persistent automatically:
def persistent_lru_cache(filename, maxsize=128, typed=False):
"""new docstring explaining what dbpath does"""
# same code as before up to here
def decorating_function(user_function):
cache = shelve.open(filename)
# same code as before from here on.
Of course that only works if your arguments are strings. And it could be a little slow.
So, you might want to instead keep it as an in-memory dict, and just write code that pickles it to a file atexit, and restores it from a file if present at startup:
def decorating_function(user_function):
# ...
try:
with open(filename, 'rb') as f:
cache = pickle.load(f)
except:
cache = {}
def cache_save():
with lock:
with open(filename, 'wb') as f:
pickle.dump(cache, f)
atexit.register(cache_save)
# …
wrapper.cache_save = cache_save
wrapper.cache_filename = filename
Or, if you want it to write every N new values (so you don't lose the whole cache on, say, an _exit or a segfault or someone pulling the cord), add this to the second and third versions of wrapper, right after the misses += 1:
if misses % N == 0:
cache_save()
See here for a working version of everything up to this point (using save_every as the "N" argument, and defaulting to 1, which you probably don't want in real life).
If you want to be really clever, maybe copy the cache and save that in a background thread.
You might want to extend the cache_info to include something like number of cache writes, number of misses since last cache write, number of entries in the cache at startup, …
And there are probably other ways to improve this.
From a quick test, with save_every=1, this makes the cache on both get_pep and fib (from the functools docs) persistent, with no measurable slowdown to get_pep and a very small slowdown to fib the first time (note that fib(100) has 100097 hits vs. 101 misses…), and of course a large speedup to get_pep (but not fib) when you re-run it. So, just what you'd expect.
I can't say I won't just use #abarnert's "ugly frame hack", but here is the version that requires you to pass in the calling module's globals dict. I think it's worth posting given that decorator functions with arguments are tricky and meaningfully different from those without arguments.
def create_if_not_exists_2(my_globals):
def wrap(func):
if "_" != func.__name__[0]:
raise Exception("Function names used in cine must begin with'_'")
my_globals.setdefault(func.__name__[1:], func)
def wrapped(*args):
func(*args)
return wrapped
return wrap
Which you can then use in a different module like this:
from functools32 import lru_cache
from cine import create_if_not_exists_2
#create_if_not_exists_2(globals())
#lru_cache()
def _square(x):
print "Squaring", x
return x*x
assert "_square" in globals()
assert "square" in globals()
I've gained enough familiarity with decorators during this process that I was comfortable taking a swing at solving the problem another way:
from functools32 import lru_cache
try:
my_cine
except NameError:
class my_cine(object):
_reg_funcs = {}
#classmethod
def func_key (cls, f):
try:
name = f.func_name
except AttributeError:
name = f.__name__
return (f.__module__, name)
def __init__(self, f):
k = self.func_key(f)
self._f = self._reg_funcs.setdefault(k, f)
def __call__(self, *args, **kwargs):
return self._f(*args, **kwargs)
if __name__ == "__main__":
#my_cine
#lru_cache()
def fact_my_cine(n):
print "In fact_my_cine for", n
if n < 2:
return 1
return fact_my_cine(n-1) * n
x = fact_my_cine(10)
print "The answer is", x
#abarnert, if you are still watching, I'd be curious to hear your assessment of the downsides of this method. I know of two:
You have to know in advance what attributes to look in for a name to associate with the function. My first stab at it only looked at func_name which failed when passed an lru_cache object.
Resetting a function is painful: del my_cine._reg_funcs[('__main__', 'fact_my_cine')], and the swing I took at adding a __delitem__ was unsuccessful.
There were several discussions on "returning multiple values in Python", e.g.
1,
2.
This is not the "multiple-value-return" pattern I'm trying to find here.
No matter what you use (tuple, list, dict, an object), it is still a single return value and you need to parse that return value (structure) somehow.
The real benefit of multiple return value is in the upgrade process. For example,
originally, you have
def func():
return 1
print func() + func()
Then you decided that func() can return some extra information but you don't want to break previous code (or modify them one by one). It looks like
def func():
return 1, "extra info"
value, extra = func()
print value # 1 (expected)
print extra # extra info (expected)
print func() + func() # (1, 'extra info', 1, 'extra info') (not expected, we want the previous behaviour, i.e. 2)
The previous codes (func() + func()) are broken. You have to fix it.
I don't know whether I made the question clear... You can see the CLISP example. Is there an equivalent way to implement this pattern in Python?
EDIT: I put the above clisp snippets online for your quick reference.
Let me put two use cases here for multiple return value pattern. Probably someone can have alternative solutions to the two cases:
Better support smooth upgrade. This is shown in the above example.
Have simpler client side codes. See following alternative solutions I have so far. Using exception can make the upgrade process smooth but it costs more codes.
Current alternatives: (they are not "multi-value-return" constructions, but they can be engineering solutions that satisfy some of the points listed above)
tuple, list, dict, an object. As is said, you need certain parsing from the client side. e.g. if ret.success == True: blabla. You need to ret = func() before that. It's much cleaner to write if func() == True: blabal.
Use Exception. As is discussed in this thread, when the "False" case is rare, it's a nice solution. Even in this case, the client side code is still too heavy.
Use an arg, e.g. def func(main_arg, detail=[]). The detail can be list or dict or even an object depending on your design. The func() returns only original simple value. Details go to the detail argument. Problem is that the client need to create a variable before invocation in order to hold the details.
Use a "verbose" indicator, e.g. def func(main_arg, verbose=False). When verbose == False (default; and the way client is using func()), return original simple value. When verbose == True, return an object which contains simple value and the details.
Use a "version" indicator. Same as "verbose" but we extend the idea there. In this way, you can upgrade the returned object for multiple times.
Use global detail_msg. This is like the old C-style error_msg. In this way, functions can always return simple values. The client side can refer to detail_msg when necessary. One can put detail_msg in global scope, class scope, or object scope depending on the use cases.
Use generator. yield simple_return and then yield detailed_return. This solution is nice in the callee's side. However, the caller has to do something like func().next() and func().next().next(). You can wrap it with an object and override the __call__ to simplify it a bit, e.g. func()(), but it looks unnatural from the caller's side.
Use a wrapper class for the return value. Override the class's methods to mimic the behaviour of original simple return value. Put detailed data in the class. We have adopted this alternative in our project in dealing with bool return type. see the relevant commit: https://github.com/fqj1994/snsapi/commit/589f0097912782ca670568fe027830f21ed1f6fc (I don't have enough reputation to put more links in the post... -_-//)
Here are some solutions:
Based on #yupbank 's answer, I formalized it into a decorator, see github.com/hupili/multiret
The 8th alternative above says we can wrap a class. This is the current engineering solution we adopted. In order to wrap more complex return values, we may use meta class to generate the required wrapper class on demand. Have not tried, but this sounds like a robust solution.
try inspect?
i did some try, and not very elegant, but at least is doable.. and works :)
import inspect
from functools import wraps
import re
def f1(*args):
return 2
def f2(*args):
return 3, 3
PATTERN = dict()
PATTERN[re.compile('(\w+) f()')] = f1
PATTERN[re.compile('(\w+), (\w+) = f()')] = f2
def execute_method_for(call_str):
for regex, f in PATTERN.iteritems():
if regex.findall(call_str):
return f()
def multi(f1, f2):
def liu(func):
#wraps(func)
def _(*args, **kwargs):
frame,filename,line_number,function_name,lines,index=\
inspect.getouterframes(inspect.currentframe())[1]
call_str = lines[0].strip()
return execute_method_for(call_str)
return _
return liu
#multi(f1, f2)
def f():
return 1
if __name__ == '__main__':
print f()
a, b = f()
print a, b
Your case does need code editing. However, if you need a hack, you can use function attributes to return extra values , without modifying return values.
def attr_store(varname, value):
def decorate(func):
setattr(func, varname, value)
return func
return decorate
#attr_store('extra',None)
def func(input_str):
func.extra = {'hello':input_str + " ,How r you?", 'num':2}
return 1
print(func("John")+func("Matt"))
print(func.extra)
Demo : http://codepad.org/0hJOVFcC
However, be aware that function attributes will behave like static variables, and you will need to assign values to them with care, appends and other modifiers will act on previous saved values.
the magic is you should use design pattern blablabla to not use actual operation when you process the result, but use a parameter as the operation method, for your case, you can use the following code:
def x():
#return 1
return 1, 'x'*1
def f(op, f1, f2):
print eval(str(f1) + op + str(f2))
f('+', x(), x())
if you want generic solution for more complicated situation, you can extend the f function, and specify the process operation via the op parameter