Is there any way to write unittests or doctests for innerfunc?
def outerfunc():
def innerfunc():
do_something()
return innerfunc()
Only if you provide a way to extract the inner function object itself, e.g.
def outerfunc(calltheinner=True):
def innerfunc():
do_something()
if calltheinner:
return innerfunc()
else:
return innerfunc
If your outer function insists on hiding the inner one entirely inside itself (never letting it percolate outside when properly cajoled to do so), your unit-tests are powerless to defeat this strong bid for extreme and total privacy;-).
This is actually an old open Python issue:
Issue 1650090: doctest doesn't find nested functions
There's a candidate patch (from 2007) that makes doctest find nested functions, but someone probably needs to push this.
Related
Below is the basic logic for function foo:
def foo(item_lst):
val_in_foo_scope = 1
for item in item_lst:
# some logic to deal with item
# val_in_foo_scope used
pass
return 0
The logic in the loop can be very complex, in order to make the code more clear, I want to split the logic with a separate function.
With inner function:
def foo(item_lst):
val_in_foo_scope = 1
def some_logic(item):
# val_in_foo_scope used
pass
for item in item_lst:
some_logic(item)
return 0
With outer function:
def some_logic(item, val):
# val used
pass
def foo(item_lst):
val_in_foo_scope = 1
for item in item_lst:
some_logic(item, val_in_foo_scope)
return 0
The inner function version
val_in_foo_scope can be used directly -- good
we can easily know that the some_logic is relevant with foo, actually only be used in function foo -- good
each time function foo is called, a new inner function will be created -- not so good
The outer function version
val_in_foo_scope can not be used directly -- not so good
we can not see the relevance between some_logic and foo directly -- not so good
some_logic will be created one time -- good
there will be so many functions in the global namespace -- not so good
So, which solution is better or is there any other solutions?
Factors below or any other factors you come up with can be considered：
val_in_foo_scope is used or not
whether the time cost to create inner function each time can be ignored
Use lambda if it's a simple function.
Use an inner function if it is complex and you don't want to make it "public".
Use a "private" method if you want to mark it hidden and uses members of the instance.
Use a method if you want to make it "public" and uses members of the instance.
Use a class method if it uses class members.
And lastly use a global function if it's general enough to be used by other classes/functions.
You forgot one point in your pro/cons list: testability. Keeping some_logic out of foo makes it testable in isolation, which is important if it's indeed a "complex" (hence very probably critical) function.
As a general rule, only use inner functions when you have both of those conditions: it's trivial stuff and passing the required context (the 'outer' function's context) would be a pain.
(nb: I'm of course not talking about using inner functions for closures - like in a decorator - here).
I have a test below using mock on Python 2.6.
#patch('testme.check.RE.query')
def test_check(query):
query.filter.one = MagicMock(return_value=None)
....
assert against_experiment(cl, ei, cd)
Inside the module I want to test, I have
def check_against_experiment(c, e, c):
re = RE.query.filter(RE.e_id == e).one()
Two questions:
When inspecting re = RE.query.filter(RE.e_id == e).one(), I see <MagicMock name='query.filter().one()' id='62856528'>.
Why isn't this returning None?
Another question is when I change #path('testme.check.RE.query') to #path('testme.check.RE.query') and change query.filter.one to query.one, I notice that nothing is mocked out. Why is this the case?
When you are calling RE.query.filter(RE.e_id == e) in your function-under-test, it is returning a new unconfigured MagicMock, and then you are calling its one function, which is generating that default return_value.
You probably want to set the return_value of filter to be a new Mock whose one function returns None.
Short answer is what you want to do is
query.filter.return_value.one.return_value = None
Default mock's return_value attribute is a MagickMock object (AFAIK allocated as lazy property).
IMHO try to avoid this kind of mock configurations, take your class/function more clear and narrow to avoid object browsing. These kind of test make source and tests tangled and create a solid fence to do refactoring.
For your second question I can just point you to Where to Patch must read document. From what you say is impossible to find the root cause by I can bet the answer is always in this documentation's chapter.
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.
OK, I know it's going to be obvious, but I cannot work out how to write a test for an internal function. Here's a trivial piece of code to illustrate the problem.
def high(x, y):
def low(x):
return x*2
return y*low(x)
class TestHigh(unittest.TestCase):
def test_high(self):
self.assertEqual(high(1,2),4)
def test_low(self):
self.assertEqual(low(3),6)
results in
Exception: NameError: global name 'low' is not defined
In the "real" case I want to be able to test the lower level function in isolation to make sure all the paths are exercised, which is cumbersome when testing only from the higher level.
low is nested within the high function, so it's not accessible from outside the function. The equivalent for your function would be high(3,1)
You write tests to ensure that the publicly visible interface performs according to its specification. You should not attempt to write tests for internal functionality that is not exposed.
If you cannot fully test low() through the results of high() then the untested parts of low() cannot matter to anything outside.
BAD: Try making a class and adding the functions as methods (or staticfunctions) to it.
(I'll leave this here as a reference for what NOT to do.)
GOOD: Write module level functions or accept that you can't test it if you nest it.
I was working with generator functions and private functions of a class. I am wondering
Why when yielding (which in my one case was by accident) in __someFunc that this function just appears not to be called from within __someGenerator. Also what is the terminology I want to use when referring to these aspects of the language?
Can the python interpreter warn of such instances?
Below is an example snippet of my scenario.
class someClass():
def __init__(self):
pass
#Copy and paste mistake where yield ended up in a regular function
def __someFunc(self):
print "hello"
#yield True #if yielding in this function it isn't called
def __someGenerator (self):
for i in range(0, 10):
self.__someFunc()
yield True
yield False
def someMethod(self):
func = self.__someGenerator()
while func.next():
print "next"
sc = someClass()
sc.someMethod()
I got burned on this and spent some time trying to figure out why a function just wasn't getting called. I finally discovered I was yielding in function I didn't want to in.
A "generator" isn't so much a language feature, as a name for functions that "yield." Yielding is pretty much always legal. There's not really any way for Python to know that you didn't "mean" to yield from some function.
This PEP http://www.python.org/dev/peps/pep-0255/ talks about generators, and may help you understand the background better.
I sympathize with your experience, but compilers can't figure out what you "meant for them to do", only what you actually told them to do.
I'll try to answer the first of your questions.
A regular function, when called like this:
val = func()
executes its inside statements until it ends or a return statement is reached. Then the return value of the function is assigned to val.
If a compiler recognizes the function to actually be a generator and not a regular function (it does that by looking for yield statements inside the function -- if there's at least one, it's a generator), the scenario when calling it the same way as above has different consequences. Upon calling func(), no code inside the function is executed, and a special <generator> value is assigned to val. Then, the first time you call val.next(), the actual statements of func are being executed until a yield or return is encountered, upon which the execution of the function stops, value yielded is returned and generator waits for another call to val.next().
That's why, in your example, function __someFunc didn't print "hello" -- its statements were not executed, because you haven't called self.__someFunc().next(), but only self.__someFunc().
Unfortunately, I'm pretty sure there's no built-in warning mechanism for programming errors like yours.
Python doesn't know whether you want to create a generator object for later iteration or call a function. But python isn't your only tool for seeing what's going on with your code. If you're using an editor or IDE that allows customized syntax highlighting, you can tell it to give the yield keyword a different color, or even a bright background, which will help you find your errors more quickly, at least. In vim, for example, you might do:
:syntax keyword Yield yield
:highlight yield ctermbg=yellow guibg=yellow ctermfg=blue guifg=blue
Those are horrendous colors, by the way. I recommend picking something better. Another option, if your editor or IDE won't cooperate, is to set up a custom rule in a code checker like pylint. An example from pylint's source tarball:
from pylint.interfaces import IRawChecker
from pylint.checkers import BaseChecker
class MyRawChecker(BaseChecker):
"""check for line continuations with '\' instead of using triple
quoted string or parenthesis
"""
__implements__ = IRawChecker
name = 'custom_raw'
msgs = {'W9901': ('use \\ for line continuation',
('Used when a \\ is used for a line continuation instead'
' of using triple quoted string or parenthesis.')),
}
options = ()
def process_module(self, stream):
"""process a module
the module's content is accessible via the stream object
"""
for (lineno, line) in enumerate(stream):
if line.rstrip().endswith('\\'):
self.add_message('W9901', line=lineno)
def register(linter):
"""required method to auto register this checker"""
linter.register_checker(MyRawChecker(linter))
The pylint manual is available here: http://www.logilab.org/card/pylint_manual
And vim's syntax documentation is here: http://www.vim.org/htmldoc/syntax.html
Because the return keyword is applicable in both generator functions and regular functions, there's nothing you could possibly check (as #Christopher mentions). The return keyword in a generator indicates that a StopIteration exception should be raised.
If you try to return with a value from within a generator (which doesn't make sense, since return just means "stop iteration"), the compiler will complain at compile-time -- this may catch some copy-and-paste mistakes:
>>> def foo():
... yield 12
... return 15
...
File "<stdin>", line 3
SyntaxError: 'return' with argument inside generator
I personally just advise against copy and paste programming. :-)
From the PEP:
Note that return means "I'm done, and have nothing interesting to
return", for both generator functions and non-generator functions.
We do this.
Generators have names with "generate" or "gen" in their name. It will have a yield statement in the body. Pretty easy to check visually, since no method is much over 20 lines of code.
Other methods don't have "gen" in their name.
Also, we do not every use __ (double underscore) names under any circumstances. 32,000 lines of code. Non __ names.
The "generator vs. non-generator" method function is entirely a design question. What did the programmer "intend" to happen. The compiler can't easily validate your intent, it can only validate what you actually typed.