I have a Python module that I am testing, and because of the way that the module works (it does some initialization upon import) have been reloading the module during each unittest that is testing the initialization. The reload is done in the setUp method, so all tests are actually reloading the module, which is fine.
This all works great if I am only running tests in that file during any given Python session because I never required a reference to the previous instance of the module. But when I use Pydev or unittest's discover I get errors as seen here because other tests which import this module have lost their reference to objects in the module since they were imported before all of the reloading business in my tests.
There are similar questions around SO like this one, but those all deal with updating objects after reloads have occurred. What I would like to do is save the state of the module after the initial import, run my tests that do all of the reloading, and then in the test tearDown to put the initial reference to the module back so that tests that run downstream that use the module still have the correct reference. Note that I am not making any changes to the module, I am only reloading it to test some initialization pieces that it does.
There are also some solutions that include hooks in the module code which I am not interested in. I don't want to ask developers to push things into the codebase just so tests can run. I am using Python 2.6 and unittest. I see that some projects exist like process-isolation, and while I am not sure if that does entirely what I am asking for, it does not work for Python 2.6 and I don't want to add new packages to our stack if possible. Stub code follows:
import mypackage.mymodule
saved_module = mypackage.mymodule
class SomeTestThatReloads(unittest.TestCase):
def setUp(self):
reload(mypackage.mymodule)
def tearDown(self):
# What to do here with saved_module?
def test_initialization(self):
# testing scenario code
Unfortunately, there is no simple way to do that. If your module's initialization has side effects (and by the looks of it it does -- hooks, etc.), there is no automated way to undo them, short of entirely restarting the Python process.
Similarly, if anything in your code imports something from your module rather than the module itself (e.g. from my_package.my_module import some_object instead of import my_package.my_module), reloading the module won't do anything to the imported objects (some_object will refer to whatever my_package.my_module.some_object referred to when the import statement was executed, regardless of what you reload and what's on the disk).
The problem this all comes down to is that Python's module system works by executing the modules (which is full of side effects, the definition of classes/functions/variables being only one of many) and then exposing the top-level variables they created, and the Python VM itself treats modules as one big chunk of global state with no isolation.
Therefore, the general solution to your problem is to restart a new Python process after each test (which sucks :( ).
If your modules' initialization side effects are limited, you can try running your tests with Nose instead of Unittest (the tests are compatible, you don't have to rewrite anything), whose Isolate plugin attempts to do what you want: http://nose.readthedocs.org/en/latest/plugins/isolate.html
But it's not guaranteed to work in the general case, because of what I said above.
Related
I am starting a new Python project that is supposed to run both sequentially and in parallel. However, because the behavior is entirely different, running in parallel would require a completely different set of classes than those used when running sequentially. But there is so much overlap between the two codes that it makes sense to have a unified code and defer the parallel/sequential behavior to a certain group of classes.
Coming from a C++ world, I would let the user set a Parallel or Serial class in the main file and use that as a template parameter to instantiate other classes at runtime. In Python there is no compilation time so I'm looking for the most Pythonic way to accomplish this. Ideally, it would be great that the code determines whether the user is running sequentially or in parallel to select the classes automatically. So if the user runs mpirun -np 4 python __main__.py the code should behave entirely different than when the user calls just python __main__.py. Somehow it makes no sense to me to have if statements to determine the type of an object at runtime, there has to be a much more elegant way to do this. In short, I would like to avoid:
if isintance(a, Parallel):
m = ParallelObject()
elif ifinstance(a, Serial):
m = SerialObject()
I've been reading about this, and it seems I can use factories (which somewhat have this conditional statement buried in the implementation). Yet, using factories for this problem is not an option because I would have to create too many factories.
In fact, it would be great if I can just "mimic" C++'s behavior here and somehow use Parallel/Serial classes to choose classes properly. Is this even possible in Python? If so, what's the most Pythonic way to do this?
Another idea would be to detect whether the user is running in parallel or sequentially and then load the appropriate module (either from a parallel or sequential folder) with the appropriate classes. For instance, I could have the user type in the main script:
from myPackage.parallel import *
or
from myPackage.serial import *
and then have the parallel or serial folders import all shared modules. This would allow me to keep all classes that differentiate parallel/serial behavior with the same names. This seems to be the best option so far, but I'm concerned about what would happen when I'm running py.test because some test files will load parallel modules and some other test files would load the serial modules. Would testing work with this setup?
You may want to check how a similar issue is solved in the stdlib: https://github.com/python/cpython/blob/master/Lib/os.py - it's not a 100% match to your own problem, nor the only possible solution FWIW, but you can safely assume this to be a rather "pythonic" solution.
wrt/ the "automagic" thing depending on execution context, if you decide to go for it, by all means make sure that 1/ both implementations can still be explicitely imported (like os.ntpath and os.posixpath) so they are truly unit-testable, and 2/ the user can still manually force the choice.
EDIT:
So if I understand it correctly, this file you points out imports modules depending on (...)
What it "depends on" is actually mostly irrelevant (in this case it's a builtin name because the target OS is known when the runtime is compiled, but this could be an environment variable, a command line argument, a value in a config file etc). The point was about both conditional import of modules with same API but different implementations while still providing direct explicit access to those modules.
So in a similar way, I could let the user type from myPackage.parallel import * and then in myPackage/init.py I could import all the required modules for the parallel calculation. Is this what you suggest?
Not exactly. I posted this as an example of conditional imports mostly, and eventually as a way to build a "bridge" module that can automagically select the appropriate implementation at runtime (on which basis it does so is up to you).
The point is that the end user should be able to either explicitely select an implementation (by explicitely importing the right submodule - serial or parallel and using it directly) OR - still explicitely - ask the system to select one or the other depending on the context.
So you'd have myPackage.serial and myPackage.parallel (just as they are now), and an additional myPackage.automagic that dynamically selects either serial or parallel. The "recommended" choice would then be to use the "automagic" module so the same code can be run either serial or parallel without the user having to care about it, but with still the ability to force using one or the other where it makes sense.
My fear is that py.test will have modules from parallel and serial while testing different files and create a mess
Why and how would this happen ? Remember that Python has no "process-global" namespace - "globals" are really "module-level" only - and that python's import is absolutely nothing like C/C++ includes.
import loads a module object (can be built directly from python source code, or from compiled C code, or even dynamically created - remember, at runtime a module is an object, instance of the module type) and binds this object (or attributes of this object) into the enclosing scope. Also, modules are garanteed (with a couple caveats, but those are to be considered as error cases) to be imported only once for a given process (and then cached) so importing the same module twice in a same process will yield the same object (IOW a module is a singleton).
All this means that given something like
# module A
def foo():
return bar(42)
def bar(x):
return x * 2
and
# module B
def foo():
return bar(33)
def bar(x):
return x / 2
It's garanteed that however you import from A and B, A.foo will ALWAYS call A.bar and NEVER call B.bar and B.foo will only ever call B.bar (unless you explicitely monkeyptach them of course but that's not the point).
Also, this means that within a module you cannot have access to the importing namespace (the module or function that's importing your module), so you cannot have a module depending on "global" names set by the importer.
To make a long story short, you really need to forget about C++ and learn how Python works, as those are wildly different languages with wildly different object models, execution models and idioms. A couple interesting reads are http://effbot.org/zone/import-confusion.htm and https://nedbatchelder.com/text/names.html
EDIT 2:
(about the 'automagic' module)
I would do that based on whether the user runs mpirun or just python. However, it seems it's not possible (see for instance this or this) in a portable way without a hack. Any ideas in that direction?
I've never ever had anything to do with mpi so I can't help with this - but if the general consensus is that there's no reliable portable way to detect this then obviously there's your answer.
This being said, simple stupid solutions are sometimes overlooked. In your case, explicitly setting an environment variable or passing a command-line switch to your main script would JustWork(tm), ie the user should for example use
SOMEFLAG=serial python main.py
vs
SOMEFLAG=parallel mpirun -np4 python main.py
or
python main.py serial
vs
mpirun -np4 python main.py parallel
(whichever works best for you needs - is the most easily portable).
This of course requires a bit more documentation and some more effort from the end-user but well...
I'm not really what you're asking here. Python classes are just (callable/instantiable) objects themselves, so you can of course select and use them conditionally. If multiple classes within multiple modules are involved, you can also make the imports conditional.
if user_says_parallel:
from myPackage.parallel import ParallelObject
ObjectClass = ParallelObject
else:
from myPackage.serial import SerialObject
ObjectClass = SerialObject
my_abstract_object = ObjectClass()
If that's very useful depends on your classes and the effort it takes to make sure they have the same API so they're compatible when replacing each other. Maybe even inheritance à la ParallelObject => SerialObject is possible, or at least a common (virtual) base class to put all the shared code. But that's just the same as in C++.
I'm maintaining a dictionary and that is loaded inside the config file. The dictionary is loaded from a JSON file.
In config.py
name_dict = json.load(open(dict_file))
I'm importing this config file in several other scripts(file1.py, file2.py,...,filen.py) using
import config
statement. My question is when will the config.py script be executed ? I'm sure it wont be executed for every import call that is made inside my multiple scripts. But, what exactly happens when an import statement is called.
The top-level code in a module is executed once, the first time you import it. After that, the module object will be found in sys.modules, and the code will not be re-executed to re-generate it.
There are a few exceptions to this:
reload, obviously.
Accidentally importing the same module under two different names (e.g., if the module is in a package, and you've got some directory in the middle of the package in sys.path, you could end up with mypackage.mymodule and mymodule being two copies of the same thing, in which case the code gets run twice).
Installing import hooks/custom imported that replace the standard behavior.
Explicitly monkeying with sys.modules.
Directly calling functions out of imp/importlib or the like.
Certain cases with multiprocessing (and modules that use it indirectly, like concurrent.futures).
For Python 3.1 and later, this is all described in detail under The import system. In particular, look at the Searching section. (The multiprocessing-specific cases are described for that module.)
For earlier versions of Python, you pretty much have to infer the behavior from a variety of different sources and either reading the code or experimenting. However, the well-documented new behavior is intended to work like the old behavior except in specifically described ways, so you can usually get away with reading the 3.x docs even for 2.x.
Note that in general, you don't want to rely on whether top-level code in the module is run once or multiple times. For example, given a top-level function definition, as long as you never compare function objects, or rebind any globals that it (meaning the definition itself, not just the body) depends on, it doesn't make any difference. However, there are some exceptions to that, and loading start-time config files is a perfect example of an exception.
I am attempting to create a simple framework that will discover all of the test cases from a specific directory (I am using unittest for these cases) and run each of these test cases against multiples python files that will all implement the same code with the same function signatures.
Autograder.py
TestCasesFolder/
TestCase1.py
TestCase2.py
...
ImplementationFolder/
Implementation1.py
SecondImplementationFolder/
Implementation2.py
The framework succesfully finds all of the test case using (note this is in the class)
self.suites = unittest.defaultTestLoader.discover(self.testDirectory)
From there, I would like to run these suites on both Implementation1 and Implementation2.
I have been using the built in
self.suites.run(unittest.TestResult)
method from unittest to run my tests, and my first attempt at solving this problem was to import the current implementation I wanted to test using
imp.load_source
and then update the global namespace for the TestCase1.py with the correct module reference. However, because each module has its own global namespace I'm not sure if I can hook into the other files namespace. I am also not sure if this the correct approach, or if there is a better way than my implementation. How should I go about doing this?
EDIT
My current solution that seems to work is for the Autograder.py file to update the __builtins__ module with a reference to the Implementation module. The actual line looks like:
__builtins__.ImplementationModule = imp.load_source("Implementation Module", "Implementation1.py")
This means when the TestCase1.py has access to ImplementationModule through __builtins__. Of course the problem is this assumes that the __builtins__ module never implements anything that has the name ImplementationModule otherwise I will overwrite it with unknown implications. Is there a less risky version of doing this?
Have you looked at the nose system? It sounds very similar to what you are doing.
http://readthedocs.org/docs/nose/
In a framework such as Django, I'd imagine that if a user lands on a page (running a view function called "some_page"), and you have 8 imports at the top of module that are irrelevant to that view, you're wasting cycles on those imports. My questions are:
Is it a large enough amount of resources to make an impact on a high-traffic website?
Is it such a bad practice to import inside of a function for this purpose that it should be avoided at said impact?
Note: This could be considered premature optimization, but I'm not interested in that argument. Let's assume, for the sake of practical theory, that this is a completed site with loads of traffic, needing to be optimized in every way possible, and the application code, as well as DB have been fully optimized by 50 PhD database admins and developers, and these imports are the only thing left.
No, don't do this. In a normal python execution environment on the web (mod_wsgi, gunicorn, etc.) when your process starts those imports will be executed, and then all subsequent requests will not re-execute the script. If you put the imports inside the functions they'll have to be processed every time the function is called.
Yes, it is a bad practice to import at the function level. By using smarter imports at the top of the module, you create a one time, small cost. However, if you place an import in a function you will suffer the cost of the import each time that function is run. So, rather than import in the function, just import at the top of the module.
A few things you can do to clean up and improve your imports:
Don't use wild imports e.g. from x import *
Where possible, just use a normal import e.g. import x
Try to split your code up into smaller modules that can be called separately, so that fewer imports are made
Also, placing imports at the top of the module is a matter of style. There's a reason why PEP 8 says that modules need to be imported at the top. It's far more readable and maintainable that way.
Finally, some imports at function level will cause compatibility issues in the future, as from x import * is not valid Python 3.x at function level.
1) The answer is no. Django/Python is not like PHP. Your whole module will not be reinterpreted with each pageview like happens with PHP includes. The module will be in memory and each page view will make a simple function call to your view.
2) Yes, it will be a counter-optimization to make imports at the view level.
No. Same reason as other answers.
Yes. Same reason as other answes.
BTW, you can also do import lazily.
For example, Importing toolkit can "import" a module in top level code, but the module is not actually loaded until one of the attributes is accessed.
Sometimes following boiler-plate makes sense:
foo = None
def foorify():
global foo
if not foo: from xxx import foo
foo.bar()
This makes sense when foorification is conditional on something that rarely changes, e.g. one server foorifies while another never does or if you don't want or cannot safely import foo during application startup or most tests.
While developing a largeish project (split in several files and folders) in Python with IPython, I run into the trouble of cached imported modules.
The problem is that instructions import module only reads the module once, even if that module has changed! So each time I change something in my package, I have to quit and restart IPython. Painful.
Is there any way to properly force reloading some modules? Or, better, to somehow prevent Python from caching them?
I tried several approaches, but none works. In particular I run into really, really weird bugs, like some modules or variables mysteriously becoming equal to None...
The only sensible resource I found is Reloading Python modules, from pyunit, but I have not checked it. I would like something like that.
A good alternative would be for IPython to restart, or restart the Python interpreter somehow.
So, if you develop in Python, what solution have you found to this problem?
Edit
To make things clear: obviously, I understand that some old variables depending on the previous state of the module may stick around. That's fine by me. By why is that so difficult in Python to force reload a module without having all sort of strange errors happening?
More specifically, if I have my whole module in one file module.py then the following works fine:
import sys
try:
del sys.modules['module']
except AttributeError:
pass
import module
obj = module.my_class()
This piece of code works beautifully and I can develop without quitting IPython for months.
However, whenever my module is made of several submodules, hell breaks loose:
import os
for mod in ['module.submod1', 'module.submod2']:
try:
del sys.module[mod]
except AttributeError:
pass
# sometimes this works, sometimes not. WHY?
Why is that so different for Python whether I have my module in one big file or in several submodules? Why would that approach not work??
import checks to see if the module is in sys.modules, and if it is, it returns it. If you want import to load the module fresh from disk, you can delete the appropriate key in sys.modules first.
There is the reload builtin function which will, given a module object, reload it from disk and that will get placed in sys.modules. Edit -- actually, it will recompile the code from the file on the disk, and then re-evalute it in the existing module's __dict__. Something potentially very different than making a new module object.
Mike Graham is right though; getting reloading right if you have even a few live objects that reference the contents of the module you don't want anymore is hard. Existing objects will still reference the classes they were instantiated from is an obvious issue, but also all references created by means of from module import symbol will still point to whatever object from the old version of the module. Many subtly wrong things are possible.
Edit: I agree with the consensus that restarting the interpreter is by far the most reliable thing. But for debugging purposes, I guess you could try something like the following. I'm certain that there are corner cases for which this wouldn't work, but if you aren't doing anything too crazy (otherwise) with module loading in your package, it might be useful.
def reload_package(root_module):
package_name = root_module.__name__
# get a reference to each loaded module
loaded_package_modules = dict([
(key, value) for key, value in sys.modules.items()
if key.startswith(package_name) and isinstance(value, types.ModuleType)])
# delete references to these loaded modules from sys.modules
for key in loaded_package_modules:
del sys.modules[key]
# load each of the modules again;
# make old modules share state with new modules
for key in loaded_package_modules:
print 'loading %s' % key
newmodule = __import__(key)
oldmodule = loaded_package_modules[key]
oldmodule.__dict__.clear()
oldmodule.__dict__.update(newmodule.__dict__)
Which I very briefly tested like so:
import email, email.mime, email.mime.application
reload_package(email)
printing:
reloading email.iterators
reloading email.mime
reloading email.quoprimime
reloading email.encoders
reloading email.errors
reloading email
reloading email.charset
reloading email.mime.application
reloading email._parseaddr
reloading email.utils
reloading email.mime.base
reloading email.message
reloading email.mime.nonmultipart
reloading email.base64mime
Quitting and restarting the interpreter is the best solution. Any sort of live reloading or no-caching strategy will not work seamlessly because objects from no-longer-existing modules can exist and because modules sometimes store state and because even if your use case really does allow hot reloading it's too complicated to think about to be worth it.
With IPython comes the autoreload extension that automatically repeats an import before each function call. It works at least in simple cases, but don't rely too much on it: in my experience, an interpreter restart is still required from time to time, especially when code changes occur only on indirectly imported code.
Usage example from the linked page:
In [1]: %load_ext autoreload
In [2]: %autoreload 2
In [3]: from foo import some_function
In [4]: some_function()
Out[4]: 42
In [5]: # open foo.py in an editor and change some_function to return 43
In [6]: some_function()
Out[6]: 43
For Python version 3.4 and above
import importlib
importlib.reload(<package_name>)
from <package_name> import <method_name>
Refer below documentation for details.
There are some really good answers here already, but it is worth knowing about dreload, which is a function available in IPython which does as "deep reload". From the documentation:
The IPython.lib.deepreload module allows you to recursively reload a
module: changes made to any of its dependencies will be reloaded
without having to exit. To start using it, do:
http://ipython.org/ipython-doc/dev/interactive/reference.html#dreload
It is available as a "global" in IPython notebook (at least my version, which is running v2.0).
HTH
You can use import hook machinery described in PEP 302 to load not modules themself but some kind of proxy object that will allow you to do anything you want with underlying module object — reload it, drop reference to it etc.
Additional benefit is that your currently existing code will not require change and this additional module functionality can be torn off from a single point in code — where you actually add finder into sys.meta_path.
Some thoughts on implementing: create finder that will agree to find any module, except of builtin (you have nothing to do with builtin modules), then create loader that will return proxy object subclassed from types.ModuleType instead of real module object. Note that loader object are not forced to create explicit references to loaded modules into sys.modules, but it's strongly encouraged, because, as you have already seen, it may fail unexpectably. Proxy object should catch and forward all __getattr__, __setattr__ and __delattr__ to underlying real module it's keeping reference to. You will probably don't need to define __getattribute__ because of you would not hide real module contents with your proxy methods. So, now you should communicate with proxy in some way — you can create some special method to drop underlying reference, then import module, extract reference from returned proxy, drop proxy and hold reference to reloaded module. Phew, looks scary, but should fix your problem without reloading Python each time.
I am using PythonNet in my project. Fortunately, I found there is a command which can perfectly solve this problem.
using (Py.GIL())
{
dynamic mod = Py.Import(this.moduleName);
if (mod == null)
throw new Exception( string.Format("Cannot find module {0}. Python script may not be complied successfully or module name is illegal.", this.moduleName));
// This command works perfect for me!
PythonEngine.ReloadModule(mod);
dynamic instance = mod.ClassName();
Think twice for quitting and restarting in production
The easy solution without quitting & restarting is by using the reload from imp
import moduleA, moduleB
from imp import reload
reload (moduleB)