Related
I've run into a bit of a wall importing modules in a Python script. I'll do my best to describe the error, why I run into it, and why I'm tying this particular approach to solve my problem (which I will describe in a second):
Let's suppose I have a module in which I've defined some utility functions/classes, which refer to entities defined in the namespace into which this auxiliary module will be imported (let "a" be such an entity):
module1:
def f():
print a
And then I have the main program, where "a" is defined, into which I want to import those utilities:
import module1
a=3
module1.f()
Executing the program will trigger the following error:
Traceback (most recent call last):
File "Z:\Python\main.py", line 10, in <module>
module1.f()
File "Z:\Python\module1.py", line 3, in f
print a
NameError: global name 'a' is not defined
Similar questions have been asked in the past (two days ago, d'uh) and several solutions have been suggested, however I don't really think these fit my requirements. Here's my particular context:
I'm trying to make a Python program which connects to a MySQL database server and displays/modifies data with a GUI. For cleanliness sake, I've defined the bunch of auxiliary/utility MySQL-related functions in a separate file. However they all have a common variable, which I had originally defined inside the utilities module, and which is the cursor object from MySQLdb module.
I later realised that the cursor object (which is used to communicate with the db server) should be defined in the main module, so that both the main module and anything that is imported into it can access that object.
End result would be something like this:
utilities_module.py:
def utility_1(args):
code which references a variable named "cur"
def utility_n(args):
etcetera
And my main module:
program.py:
import MySQLdb, Tkinter
db=MySQLdb.connect(#blahblah) ; cur=db.cursor() #cur is defined!
from utilities_module import *
And then, as soon as I try to call any of the utilities functions, it triggers the aforementioned "global name not defined" error.
A particular suggestion was to have a "from program import cur" statement in the utilities file, such as this:
utilities_module.py:
from program import cur
#rest of function definitions
program.py:
import Tkinter, MySQLdb
db=MySQLdb.connect(#blahblah) ; cur=db.cursor() #cur is defined!
from utilities_module import *
But that's cyclic import or something like that and, bottom line, it crashes too. So my question is:
How in hell can I make the "cur" object, defined in the main module, visible to those auxiliary functions which are imported into it?
Thanks for your time and my deepest apologies if the solution has been posted elsewhere. I just can't find the answer myself and I've got no more tricks in my book.
Globals in Python are global to a module, not across all modules. (Many people are confused by this, because in, say, C, a global is the same across all implementation files unless you explicitly make it static.)
There are different ways to solve this, depending on your actual use case.
Before even going down this path, ask yourself whether this really needs to be global. Maybe you really want a class, with f as an instance method, rather than just a free function? Then you could do something like this:
import module1
thingy1 = module1.Thingy(a=3)
thingy1.f()
If you really do want a global, but it's just there to be used by module1, set it in that module.
import module1
module1.a=3
module1.f()
On the other hand, if a is shared by a whole lot of modules, put it somewhere else, and have everyone import it:
import shared_stuff
import module1
shared_stuff.a = 3
module1.f()
… and, in module1.py:
import shared_stuff
def f():
print shared_stuff.a
Don't use a from import unless the variable is intended to be a constant. from shared_stuff import a would create a new a variable initialized to whatever shared_stuff.a referred to at the time of the import, and this new a variable would not be affected by assignments to shared_stuff.a.
Or, in the rare case that you really do need it to be truly global everywhere, like a builtin, add it to the builtin module. The exact details differ between Python 2.x and 3.x. In 3.x, it works like this:
import builtins
import module1
builtins.a = 3
module1.f()
As a workaround, you could consider setting environment variables in the outer layer, like this.
main.py:
import os
os.environ['MYVAL'] = str(myintvariable)
mymodule.py:
import os
myval = None
if 'MYVAL' in os.environ:
myval = os.environ['MYVAL']
As an extra precaution, handle the case when MYVAL is not defined inside the module.
This post is just an observation for Python behaviour I encountered. Maybe the advices you read above don't work for you if you made the same thing I did below.
Namely, I have a module which contains global/shared variables (as suggested above):
#sharedstuff.py
globaltimes_randomnode=[]
globalist_randomnode=[]
Then I had the main module which imports the shared stuff with:
import sharedstuff as shared
and some other modules that actually populated these arrays. These are called by the main module. When exiting these other modules I can clearly see that the arrays are populated. But when reading them back in the main module, they were empty. This was rather strange for me (well, I am new to Python). However, when I change the way I import the sharedstuff.py in the main module to:
from globals import *
it worked (the arrays were populated).
Just sayin'
A function uses the globals of the module it's defined in. Instead of setting a = 3, for example, you should be setting module1.a = 3. So, if you want cur available as a global in utilities_module, set utilities_module.cur.
A better solution: don't use globals. Pass the variables you need into the functions that need it, or create a class to bundle all the data together, and pass it when initializing the instance.
The easiest solution to this particular problem would have been to add another function within the module that would have stored the cursor in a variable global to the module. Then all the other functions could use it as well.
module1:
cursor = None
def setCursor(cur):
global cursor
cursor = cur
def method(some, args):
global cursor
do_stuff(cursor, some, args)
main program:
import module1
cursor = get_a_cursor()
module1.setCursor(cursor)
module1.method()
Since globals are module specific, you can add the following function to all imported modules, and then use it to:
Add singular variables (in dictionary format) as globals for those
Transfer your main module globals to it
.
addglobals = lambda x: globals().update(x)
Then all you need to pass on current globals is:
import module
module.addglobals(globals())
Since I haven't seen it in the answers above, I thought I would add my simple workaround, which is just to add a global_dict argument to the function requiring the calling module's globals, and then pass the dict into the function when calling; e.g:
# external_module
def imported_function(global_dict=None):
print(global_dict["a"])
# calling_module
a = 12
from external_module import imported_function
imported_function(global_dict=globals())
>>> 12
The OOP way of doing this would be to make your module a class instead of a set of unbound methods. Then you could use __init__ or a setter method to set the variables from the caller for use in the module methods.
Update
To test the theory, I created a module and put it on pypi. It all worked perfectly.
pip install superglobals
Short answer
This works fine in Python 2 or 3:
import inspect
def superglobals():
_globals = dict(inspect.getmembers(
inspect.stack()[len(inspect.stack()) - 1][0]))["f_globals"]
return _globals
save as superglobals.py and employ in another module thusly:
from superglobals import *
superglobals()['var'] = value
Extended Answer
You can add some extra functions to make things more attractive.
def superglobals():
_globals = dict(inspect.getmembers(
inspect.stack()[len(inspect.stack()) - 1][0]))["f_globals"]
return _globals
def getglobal(key, default=None):
"""
getglobal(key[, default]) -> value
Return the value for key if key is in the global dictionary, else default.
"""
_globals = dict(inspect.getmembers(
inspect.stack()[len(inspect.stack()) - 1][0]))["f_globals"]
return _globals.get(key, default)
def setglobal(key, value):
_globals = superglobals()
_globals[key] = value
def defaultglobal(key, value):
"""
defaultglobal(key, value)
Set the value of global variable `key` if it is not otherwise st
"""
_globals = superglobals()
if key not in _globals:
_globals[key] = value
Then use thusly:
from superglobals import *
setglobal('test', 123)
defaultglobal('test', 456)
assert(getglobal('test') == 123)
Justification
The "python purity league" answers that litter this question are perfectly correct, but in some environments (such as IDAPython) which is basically single threaded with a large globally instantiated API, it just doesn't matter as much.
It's still bad form and a bad practice to encourage, but sometimes it's just easier. Especially when the code you are writing isn't going to have a very long life.
I’ve tried to develop a « module expander » tool for Python 3 but I've some issues.
The idea is the following : for a given Python script main.py, the tool generates a functionally equivalent Python script expanded_main.py, by replacing each import statement by the actual code of the imported module; this assumes that the Python source code of the imported is accessible. To do the job the right way, I’m using the builtin module ast of Python as well as astor, a third-party tool allowing to dump the AST back into Python source. The motivation of this import expander is to be able to compile a script into one single bytecode chunk, so the Python VM should not take care of importing modules (this could be useful for MicroPython, for instance).
The simplest case is the statement:
from import my_module1 import *
To transform this, my tool looks for a file my_module1.py and it replaces the import statement by the content of this file. Then, the expanded_main.py can access any name defined in my_module, as if the module was imported the normal way. I don’t care about subtle side effects that may reveal the trick. Also, to simplify, I treat from import my_module1 import a, b, c as the previous import (with asterisk), without caring about possible side effect. So far so good.
Now here is my point. How could you handle this flavor of import:
import my_module2
My first idea was to mimic this by creating a class having the same name as the module and copying the content of the Python file indented:
class my_module2:
# content of my_module2.py
…
This actually works for many cases but, sadly, I discovered that this has several glitches: one of these is that it fails with functions having a body referring to a global variable defined in the module. For example, consider the following two Python files:
# my_module2.py
g = "Hello"
def greetings():
print (g + " World!")
and
# main.py
import my_module2
print(my_module2.g)
my_module2.greetings()
At execution, main.py prints "Hello" and "Hello World!". Now, my expander tool shall generate this:
# expanded_main.py
class my_module2:
g = "Hello"
def greetings():
print (g + " World!")
print(my_module2.g)
my_module2.greetings()
At execution of expanded_main.py, the first print statement is OK ("Hello") but the greetings function raises an exception: NameError: name 'g' is not defined.
What happens actually is that
in the module my_module2, g is a global variable,
in the class my_module2, g is a class variable, which should be referred as my_module2.g.
Other similar side effects happens when you define functions, classes, … in my_module2.py and you want to refer to them in other functions, classes, … of the same my_module2.py.
Any idea how these problems could be solved?
Apart classes, are there other Python constructs that allow to mimic a module?
Final note: I’m aware that the tool should take care 1° of nested imports (recursion), 2° of possible multiple import of the same module. I don't expect to discuss these topics here.
You can execute the source code of a module in the scope of a function, specifically an instance method. The attributes can then be made available by defining __getattr__ on the corresponding class and keeping a copy of the initial function's locals(). Here is some sample code:
class Importer:
def __init__(self):
g = "Hello"
def greetings():
print (g + " World!")
self._attributes = locals()
def __getattr__(self, item):
return self._attributes[item]
module1 = Importer()
print(module1.g)
module1.greetings()
Nested imports are handled naturally by replacing them the same way with an instance of Importer. Duplicate imports shouldn't be a problem either.
I am not really a programmer but a computational statistician, so I may understand complex algorithms but not simple programming constructs.
My original problem is to check within a function if a module function is callable. I looked around and decided to go for a try (call function) - except (import module) to make it simple. I'd love to search sys.mod for this but I am running in some identifiability problems.
My current problem is that there are many ways of importing a function from a module: import module will define the function as module.function but from module import function will define it as function. Not to mention from module import function as myfunction. Therefore the same function can be called in several different ways.
My question is: is there a unique "signature" for a function that can be traced if the module is loaded? It would be fantastic to have the actual call alias to it.
ps besides: mod is mathematical function and sys.mod returns a list of loaded modules, but python (2.7) does not complain when you shadow the built-in mod function by doing the following, from sys import mod. I find this a bit awkward - is there any way to avoid this sort of shadowing programatically?
My original problem is to check within a function if a module function is callable.
By definition, all functions are callable. This will test if an object is callable: http://docs.python.org/library/functions.html#callable
Therefore the same function can be called in several different ways.
Yes, but it will be the same object. You can just use f is g to test if f and g are the same object.
Update: Why would you need to use a unique ID? Seriously, don't do this. You have is for identity tests, and the __hash__ method to define the hash function applicable.
It would be fantastic to have the actual call alias to it.
Not sure at all what you mean, but I think you just want it to always be one object. Which it is already.
mod is mathematical function and sys.mod returns a list of loaded modules, but python (2.7) does not complain to from sys import mod. I find this a bit awkward?
Then don't do that. You know about the import ... as syntax. Also mod is not by default in the global namespace (the operator % is for that).
Finally, python does complain about your import line:
>>> from sys import mod
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
ImportError: cannot import name mod
(Thanks to kindall for pointing this out).
Assume I have a module with the following:
def foo(): pass
bar = foo
You can easily see that they're the same functions by using is or id():
>>> import demo
>>> from demo import *
>>> demo.foo is foo
True
>>> id(demo.foo) == id(foo)
True
>>> demo.bar is foo
True
They all refer to the same code object, it's just stored under different names in the scope dictionary.
# define modulus f
def mod(a,b):
return b % a
print mod(5,2)
alias:
modulus=mod
print modulus(5,2)
this is pretty pythonic construct, and it is pretty intuitive for mathematicians
different ways of import serve to help you place a function into different "name space" for later use in your program, sometimes you wish to use a function a lot so you choose variant that is shorter to write.
you can also do something like:
myat=math.atanh
to make alias in another "name space"
and use it as:
myat(x)
as it would use math.atanh(x) - becomes shorter to write
Typical programmers approach would be define all you want to use and then use it. What you are trying in my belief is to do it "lazy" => import module when you need a function. That is why you wish to know if function is "callable".
Python is not functional programming language (e.g. like haskel) so that you can load or refer "on demand".
hope this helps.
This question already has answers here:
How do I unload (reload) a Python module?
(22 answers)
Closed 5 years ago.
I often test my module in the Python Interpreter, and when I see an error, I quickly update the .py file. But how do I make it reflect on the Interpreter ? So, far I have been exiting and reentering the Interpreter because re importing the file again is not working for me.
Update for Python3: (quoted from the already-answered answer, since the last edit/comment here suggested a deprecated method)
In Python 3, reload was moved to the imp module. In 3.4, imp was deprecated in favor of importlib, and reload was added to the latter. When targeting 3 or later, either reference the appropriate module when calling reload or import it.
Takeaway:
Python3 >= 3.4: importlib.reload(packagename)
Python3 < 3.4: imp.reload(packagename)
Python2: continue below
Use the reload builtin function:
https://docs.python.org/2/library/functions.html#reload
When reload(module) is executed:
Python modules’ code is recompiled and the module-level code reexecuted, defining a new set of objects which are bound to names in the module’s dictionary. The init function of extension modules is not called a second time.
As with all other objects in Python the old objects are only reclaimed after their reference counts drop to zero.
The names in the module namespace are updated to point to any new or changed objects.
Other references to the old objects (such as names external to the module) are not rebound to refer to the new objects and must be updated in each namespace where they occur if that is desired.
Example:
# Make a simple function that prints "version 1"
shell1$ echo 'def x(): print "version 1"' > mymodule.py
# Run the module
shell2$ python
>>> import mymodule
>>> mymodule.x()
version 1
# Change mymodule to print "version 2" (without exiting the python REPL)
shell2$ echo 'def x(): print "version 2"' > mymodule.py
# Back in that same python session
>>> reload(mymodule)
<module 'mymodule' from 'mymodule.pyc'>
>>> mymodule.x()
version 2
All the answers above about reload() or imp.reload() are deprecated.
reload() is no longer a builtin function in python 3 and imp.reload() is marked deprecated (see help(imp)).
It's better to use importlib.reload() instead.
So, far I have been exiting and reentering the Interpreter because re importing the file again is not working for me.
Yes, just saying import again gives you the existing copy of the module from sys.modules.
You can say reload(module) to update sys.modules and get a new copy of that single module, but if any other modules have a reference to the original module or any object from the original module, they will keep their old references and Very Confusing Things will happen.
So if you've got a module a, which depends on module b, and b changes, you have to ‘reload b’ followed by ‘reload a’. If you've got two modules which depend on each other, which is extremely common when those modules are part of the same package, you can't reload them both: if you reload p.a it'll get a reference to the old p.b, and vice versa. The only way to do it is to unload them both at once by deleting their items from sys.modules, before importing them again. This is icky and has some practical pitfalls to do with modules entries being None as a failed-relative-import marker.
And if you've got a module which passes references to its objects to system modules — for example it registers a codec, or adds a warnings handler — you're stuck; you can't reload the system module without confusing the rest of the Python environment.
In summary: for all but the simplest case of one self-contained module being loaded by one standalone script, reload() is very tricky to get right; if, as you imply, you are using a ‘package’, you will probably be better off continuing to cycle the interpreter.
In Python 3, the behaviour changes.
>>> import my_stuff
... do something with my_stuff, then later:
>>>> import imp
>>>> imp.reload(my_stuff)
and you get a brand new, reloaded my_stuff.
No matter how many times you import a module, you'll get the same copy of the module from sys.modules - which was loaded at first import mymodule
I am answering this late, as each of the above/previous answer has a bit of the answer, so I am attempting to sum it all up in a single answer.
Using built-in function:
For Python 2.x - Use the built-in reload(mymodule) function.
For Python 3.x - Use the imp.reload(mymodule).
For Python 3.4 - In Python 3.4 imp has been deprecated in favor of importlib i.e. importlib.reload(mymodule)
Few caveats:
It is generally not very useful to reload built-in or dynamically
loaded modules. Reloading sys, __main__, builtins and other key
modules is not recommended.
In many cases extension modules are not
designed to be initialized more than once, and may fail in arbitrary
ways when reloaded. If a module imports objects from another module
using from ... import ..., calling reload() for the other module does
not redefine the objects imported from it — one way around this is to
re-execute the from statement, another is to use import and qualified
names (module.name) instead.
If a module instantiates instances of a
class, reloading the module that defines the class does not affect
the method definitions of the instances — they continue to use the
old class definition. The same is true for derived classes.
External packages:
reimport - Reimport currently supports Python 2.4 through 2.7.
xreload- This works by executing the module in a scratch namespace, and then
patching classes, methods and functions in place. This avoids the
need to patch instances. New objects are copied into the target
namespace.
livecoding - Code reloading allows a running application to change its behaviour in response to changes in the Python scripts it uses. When the library detects a Python script has been modified, it reloads that script and replaces the objects it had previously made available for use with newly reloaded versions. As a tool, it allows a programmer to avoid interruption to their workflow and a corresponding loss of focus. It enables them to remain in a state of flow. Where previously they might have needed to restart the application in order to put changed code into effect, those changes can be applied immediately.
Short answer:
try using reimport: a full featured reload for Python.
Longer answer:
It looks like this question was asked/answered prior to the release of reimport, which bills itself as a "full featured reload for Python":
This module intends to be a full featured replacement for Python's reload function. It is targeted towards making a reload that works for Python plugins and extensions used by longer running applications.
Reimport currently supports Python 2.4 through 2.6.
By its very nature, this is not a completely solvable problem. The goal of this module is to make the most common sorts of updates work well. It also allows individual modules and package to assist in the process. A more detailed description of what happens is on the overview page.
Note: Although the reimport explicitly supports Python 2.4 through 2.6, I've been trying it on 2.7 and it seems to work just fine.
Basically reload as in allyourcode's asnwer. But it won't change underlying the code of already instantiated object or referenced functions. Extending from his answer:
#Make a simple function that prints "version 1"
shell1$ echo 'def x(): print "version 1"' > mymodule.py
# Run the module
shell2$ python
>>> import mymodule
>>> mymodule.x()
version 1
>>> x = mymodule.x
>>> x()
version 1
>>> x is mymodule.x
True
# Change mymodule to print "version 2" (without exiting the python REPL)
shell2$ echo 'def x(): print "version 2"' > mymodule.py
# Back in that same python session
>>> reload(mymodule)
<module 'mymodule' from 'mymodule.pyc'>
>>> mymodule.x()
version 2
>>> x()
version 1
>>> x is mymodule.x
False
Not sure if this does all expected things, but you can do just like that:
>>> del mymodule
>>> import mymodule
import sys
del sys.modules['module_name']
See here for a good explanation of how your dependent modules won't be reloaded and the effects that can have:
http://pyunit.sourceforge.net/notes/reloading.html
The way pyunit solved it was to track dependent modules by overriding __import__ then to delete each of them from sys.modules and re-import. They probably could've just reload'ed them, though.
dragonfly's answer worked for me (python 3.4.3).
import sys
del sys.modules['module_name']
Here is a lower level solution :
exec(open("MyClass.py").read(), globals())
The __debug__ variable is handy in part because it affects every module. If I want to create another variable that works the same way, how would I do it?
The variable (let's be original and call it 'foo') doesn't have to be truly global, in the sense that if I change foo in one module, it is updated in others. I'd be fine if I could set foo before importing other modules and then they would see the same value for it.
If you need a global cross-module variable maybe just simple global module-level variable will suffice.
a.py:
var = 1
b.py:
import a
print a.var
import c
print a.var
c.py:
import a
a.var = 2
Test:
$ python b.py
# -> 1 2
Real-world example: Django's global_settings.py (though in Django apps settings are used by importing the object django.conf.settings).
I don't endorse this solution in any way, shape or form. But if you add a variable to the __builtin__ module, it will be accessible as if a global from any other module that includes __builtin__ -- which is all of them, by default.
a.py contains
print foo
b.py contains
import __builtin__
__builtin__.foo = 1
import a
The result is that "1" is printed.
Edit: The __builtin__ module is available as the local symbol __builtins__ -- that's the reason for the discrepancy between two of these answers. Also note that __builtin__ has been renamed to builtins in python3.
I believe that there are plenty of circumstances in which it does make sense and it simplifies programming to have some globals that are known across several (tightly coupled) modules. In this spirit, I would like to elaborate a bit on the idea of having a module of globals which is imported by those modules which need to reference them.
When there is only one such module, I name it "g". In it, I assign default values for every variable I intend to treat as global. In each module that uses any of them, I do not use "from g import var", as this only results in a local variable which is initialized from g only at the time of the import. I make most references in the form g.var, and the "g." serves as a constant reminder that I am dealing with a variable that is potentially accessible to other modules.
If the value of such a global variable is to be used frequently in some function in a module, then that function can make a local copy: var = g.var. However, it is important to realize that assignments to var are local, and global g.var cannot be updated without referencing g.var explicitly in an assignment.
Note that you can also have multiple such globals modules shared by different subsets of your modules to keep things a little more tightly controlled. The reason I use short names for my globals modules is to avoid cluttering up the code too much with occurrences of them. With only a little experience, they become mnemonic enough with only 1 or 2 characters.
It is still possible to make an assignment to, say, g.x when x was not already defined in g, and a different module can then access g.x. However, even though the interpreter permits it, this approach is not so transparent, and I do avoid it. There is still the possibility of accidentally creating a new variable in g as a result of a typo in the variable name for an assignment. Sometimes an examination of dir(g) is useful to discover any surprise names that may have arisen by such accident.
Define a module ( call it "globalbaz" ) and have the variables defined inside it. All the modules using this "pseudoglobal" should import the "globalbaz" module, and refer to it using "globalbaz.var_name"
This works regardless of the place of the change, you can change the variable before or after the import. The imported module will use the latest value. (I tested this in a toy example)
For clarification, globalbaz.py looks just like this:
var_name = "my_useful_string"
You can pass the globals of one module to onother:
In Module A:
import module_b
my_var=2
module_b.do_something_with_my_globals(globals())
print my_var
In Module B:
def do_something_with_my_globals(glob): # glob is simply a dict.
glob["my_var"]=3
Global variables are usually a bad idea, but you can do this by assigning to __builtins__:
__builtins__.foo = 'something'
print foo
Also, modules themselves are variables that you can access from any module. So if you define a module called my_globals.py:
# my_globals.py
foo = 'something'
Then you can use that from anywhere as well:
import my_globals
print my_globals.foo
Using modules rather than modifying __builtins__ is generally a cleaner way to do globals of this sort.
You can already do this with module-level variables. Modules are the same no matter what module they're being imported from. So you can make the variable a module-level variable in whatever module it makes sense to put it in, and access it or assign to it from other modules. It would be better to call a function to set the variable's value, or to make it a property of some singleton object. That way if you end up needing to run some code when the variable's changed, you can do so without breaking your module's external interface.
It's not usually a great way to do things — using globals seldom is — but I think this is the cleanest way to do it.
I wanted to post an answer that there is a case where the variable won't be found.
Cyclical imports may break the module behavior.
For example:
first.py
import second
var = 1
second.py
import first
print(first.var) # will throw an error because the order of execution happens before var gets declared.
main.py
import first
On this is example it should be obvious, but in a large code-base, this can be really confusing.
I wondered if it would be possible to avoid some of the disadvantages of using global variables (see e.g. http://wiki.c2.com/?GlobalVariablesAreBad) by using a class namespace rather than a global/module namespace to pass values of variables. The following code indicates that the two methods are essentially identical. There is a slight advantage in using class namespaces as explained below.
The following code fragments also show that attributes or variables may be dynamically created and deleted in both global/module namespaces and class namespaces.
wall.py
# Note no definition of global variables
class router:
""" Empty class """
I call this module 'wall' since it is used to bounce variables off of. It will act as a space to temporarily define global variables and class-wide attributes of the empty class 'router'.
source.py
import wall
def sourcefn():
msg = 'Hello world!'
wall.msg = msg
wall.router.msg = msg
This module imports wall and defines a single function sourcefn which defines a message and emits it by two different mechanisms, one via globals and one via the router function. Note that the variables wall.msg and wall.router.message are defined here for the first time in their respective namespaces.
dest.py
import wall
def destfn():
if hasattr(wall, 'msg'):
print 'global: ' + wall.msg
del wall.msg
else:
print 'global: ' + 'no message'
if hasattr(wall.router, 'msg'):
print 'router: ' + wall.router.msg
del wall.router.msg
else:
print 'router: ' + 'no message'
This module defines a function destfn which uses the two different mechanisms to receive the messages emitted by source. It allows for the possibility that the variable 'msg' may not exist. destfn also deletes the variables once they have been displayed.
main.py
import source, dest
source.sourcefn()
dest.destfn() # variables deleted after this call
dest.destfn()
This module calls the previously defined functions in sequence. After the first call to dest.destfn the variables wall.msg and wall.router.msg no longer exist.
The output from the program is:
global: Hello world!
router: Hello world!
global: no message
router: no message
The above code fragments show that the module/global and the class/class variable mechanisms are essentially identical.
If a lot of variables are to be shared, namespace pollution can be managed either by using several wall-type modules, e.g. wall1, wall2 etc. or by defining several router-type classes in a single file. The latter is slightly tidier, so perhaps represents a marginal advantage for use of the class-variable mechanism.
This sounds like modifying the __builtin__ name space. To do it:
import __builtin__
__builtin__.foo = 'some-value'
Do not use the __builtins__ directly (notice the extra "s") - apparently this can be a dictionary or a module. Thanks to ΤΖΩΤΖΙΟΥ for pointing this out, more can be found here.
Now foo is available for use everywhere.
I don't recommend doing this generally, but the use of this is up to the programmer.
Assigning to it must be done as above, just setting foo = 'some-other-value' will only set it in the current namespace.
I use this for a couple built-in primitive functions that I felt were really missing. One example is a find function that has the same usage semantics as filter, map, reduce.
def builtin_find(f, x, d=None):
for i in x:
if f(i):
return i
return d
import __builtin__
__builtin__.find = builtin_find
Once this is run (for instance, by importing near your entry point) all your modules can use find() as though, obviously, it was built in.
find(lambda i: i < 0, [1, 3, 0, -5, -10]) # Yields -5, the first negative.
Note: You can do this, of course, with filter and another line to test for zero length, or with reduce in one sort of weird line, but I always felt it was weird.
I could achieve cross-module modifiable (or mutable) variables by using a dictionary:
# in myapp.__init__
Timeouts = {} # cross-modules global mutable variables for testing purpose
Timeouts['WAIT_APP_UP_IN_SECONDS'] = 60
# in myapp.mod1
from myapp import Timeouts
def wait_app_up(project_name, port):
# wait for app until Timeouts['WAIT_APP_UP_IN_SECONDS']
# ...
# in myapp.test.test_mod1
from myapp import Timeouts
def test_wait_app_up_fail(self):
timeout_bak = Timeouts['WAIT_APP_UP_IN_SECONDS']
Timeouts['WAIT_APP_UP_IN_SECONDS'] = 3
with self.assertRaises(hlp.TimeoutException) as cm:
wait_app_up(PROJECT_NAME, PROJECT_PORT)
self.assertEqual("Timeout while waiting for App to start", str(cm.exception))
Timeouts['WAIT_JENKINS_UP_TIMEOUT_IN_SECONDS'] = timeout_bak
When launching test_wait_app_up_fail, the actual timeout duration is 3 seconds.