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.
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 have two specific situations where I don't understand how importing works in Python:
1st specific situation:
When I import the same module in two different Python scripts, the module isn't imported twice, right? The first time Python encounters it, it is imported, and second time, does it check if the module has been imported, or does it make a copy?
2nd specific situation:
Consider the following module, called bla.py:
a = 10
And then, we have foo.py, a module which imports bla.py:
from bla import *
def Stuff ():
return a
And after that, we have a script called bar.py, which gets executed by the user:
from foo import *
Stuff() #This should return 10
a = 5
Stuff()
Here I don't know: Does Stuff() return 10 or 5?
Part 1
The module is only loaded once, so there is no performance loss by importing it again. If you actually wanted it to be loaded/parsed again, you'd have to reload() the module.
The first place checked is sys.modules, the cache of all modules that have been imported previously. [source]
Part 2
from foo import * imports a to the local scope. When assigning a value to a, it is replaced with the new value - but the original foo.a variable is not touched.
So unless you import foo and modify foo.a, both calls will return the same value.
For a mutable type such as a list or dict it would be different, modifying it would indeed affect the original variable - but assigning a new value to it would still not modify foo.whatever.
If you want some more detailed information, have a look at http://docs.python.org/reference/executionmodel.html:
The following constructs bind names: formal parameters to functions, import statements, class and function definitions (these bind the class or function name in the defining block), and targets that are identifiers if occurring in an assignment, for loop header, in the second position of an except clause header or after as in a with statement.
The two bold sections are the relevant ones for you: First the name a is bound to the value of foo.a during the import. Then, when doing a = 5, the name a is bound to 5. Since modifying a list/dict does not cause any binding, those operations would modify the original one (b and foo.b are bound to the same object on which you operate). Assigning a new object to b would be a binding operation again and thus separate b from foo.b.
It is also worth noting what exactly the import statement does:
import foo binds the module name to the module object in the current scope, so if you modify foo.whatever, you will work with the name in that module - any modifications/assignments will affect the variable in the module.
from foo import bar binds the given name(s) only (i.e. foo will remain unbound) to the element with the same name in foo - so operations on bar behave like explained earlier.
from foo import * behaves like the previous one, but it imports all global names which are not prefixed with an underscore. If the module defines __all__ only names inside this sequence are imported.
Part 3 (which doesn't even exist in your question :p)
The python documentation is extremely good and usually verbose - you find answer on almost every possible language-related question in there. Here are some useful links:
http://docs.python.org/reference/datamodel.html (classes, properties, magic methods, etc.) ()
http://docs.python.org/reference/executionmodel.html (how variables work in python)
http://docs.python.org/reference/expressions.html
http://docs.python.org/reference/simple_stmts.html (statements such as import, yield)
http://docs.python.org/reference/compound_stmts.html (block statements such as for, try, with)
To answer your first question:
No, python does not get 'imported' twice. When python loads a module, it checks for the module in sys.modules. If it is not in there, it is put in there, and loaded.
To answer your second question:
Modules can define what names they will export to a from camelot import * scenario, and the behavior is to create names for the existing values, not to reference existing variables (python does not have references).
On a somewhat related topic, doing a from camelot import * is not the same as a regular import.
Backstory:
I was trying implementing one way to handle -v parameters to increase the verbosity of an application. To that end, I wanted to use a global variable that is pointing to an empty lambda function initially. If -v is given the variable is changed and gets another lambda function assigned that does print it input.
MWE:
I noticed that this did not work as expected when calling the lambda function from another module after importing it via from x import *...
mwe.py:
from mod import *
import mod
def f():
vprint("test in f")
vprint("test before")
print("before: %d" % foo)
set_verbosity(1)
vprint("test after")
print("after: %d" % foo)
f()
mod.vprint("explicit: %d" % mod.foo)
modf()
mod.py:
vprint = lambda *a, **k: None
foo = 42
def set_verbosity(verbose):
global vprint, foo
if verbose > 0:
vprint = lambda *args, **kwargs: print(*args, **kwargs)
foo = 0
def modf():
vprint("modf: %d" % foo)
The output is
before: 42
after: 42
explicit: 0
modf: 0
where the "explicit" and "modf" outputs are due to the mod.vprint and modf calls at the end of the mwe. All other invocations of vprint (that go through the imported version of vprint) are apparently not using the updated definition. Likewise, the value of foo seems to be imported only once.
Question:
It looks to me as if the from x import * type of imports copies the state of the globals of the imported module. I am not so much interested in workarounds per se but the actual reason for this behavior. Where is this defined in the documentation and what's the rationale?
Workaround:
As a side note, one way to implement this anyway is to wrap the global lambda variables by functions and export only them:
_vprint = lambda *a, **k: None
def vprint(*args, **kwargs):
_vprint(*args, **kwargs)
def set_verbosity(verbose):
global _vprint
if verbose > 0:
_vprint = lambda *args, **kwargs: print(*args, **kwargs)
This makes it work with the import-from way that allows the other module to simply call vprint instead of explicitly deferencing via the module's name.
TL;DR: when you do from module import *, you're copying the names and their associated references; changing the reference associated with the original name does not change the reference associated with the copy.
This deals with the underlying difference between names and references. The underlying reason for the behavior has to do with how python handles such things.
Only one thing is truly immutable in python: memory. You can't change individual bytes directly. However, almost everything in python deals with references to individual bytes, and you can change those references. When you do my_list[2] = 5, you're not changing any memory - rather, you're allocating a new block of memory to hold the value 5, and pointing the second index of my_list to it. The original data that my_list[2] used to be pointing at is still there, but since nothing refers to it any more, the garbage collector will take care of it eventually and free the memory it was using.
The same principle goes with names. Any given namespace in python is comparable to a dict - each name has a corresponding reference. And this is where the problems come in.
Consider the difference between the following two statements:
from module import *
import module
In both cases, module is loaded into memory.
In the latter case, only one thing is added to the local namespace - the name 'module', which references the entire memory block containing the module that was just loaded. Or, well, it references the memory block of that module's own namespace, which itself has references to all the names in the module, and so on all the way down.
In the former case, however, every name in module's namespace is copied into the local namespace. The same block of memory still exists, but instead of having one reference to all of it, you now have many references to small parts of it.
Now, let's say we do both of those statements in succession:
from module import *
import module
This leaves us with one name 'module' referencing all the memory the module was loaded into, and a bunch of other names that reference individual parts of that block. We can verify that they point to the same thing:
print(module.func_name == func_name)
# True
But now, we try to assign something else to module.attribute:
module.func_name = lambda x:pass
print(module.func_name == func_name)
# False
It's no longer the same. Why?
Well, when we did module.func_name = lambda x:pass, we first allocated some memory to store lambda x:pass, and then we changed module's 'func_name' name to reference that memory instead of what it was referencing. Note that, like the example I gave earlier with lists, we didn't change the thing that the module.func_name was previously referencing - it still exists, and the local func_name continues to reference it.
So when you do from module import *, you're copying the names and their associated references; changing the reference associated with the original name does not change the reference associated with the copy.
The workaround for this is to not do import *. In fact, this is pretty much the ultimate reason why using import * is usually considered poor practice, save for a handful of special cases. Consider the following code:
# module.py
variable = "Original"
# file1.py
import module
def func1():
module.variable = "New"
# file2.py
import module
import file1
print(module.variable)
file1.func1()
print(module.variable)
When you run python file2.py, you get the following output:
Original
New
Why? Because file1 and file2 both imported module, and in both of their namespaces 'module' is pointing to the same block of memory. module's namespace contains a name 'variable' referencing some value. Then, the following things happen:
file2 says "okay, module, please give me the value associated with the name 'variable' in your namespace.
file1.func1() says "okay, module, the name 'variable' in your namespace now references this other value.
file2 says "okay, module, please give me the value associated with the name 'variable' in your namespace.
Since file1 and file2 are still both talking to the same bit of memory, they stay coordinated.
Random stab in the dark ahead:
If you look at the docs for __import__, there's the bit:
On the other hand, the statement from spam.ham import eggs, sausage as saus results in
_temp = __import__('spam.ham', globals(), locals(), ['eggs', 'sausage'], 0)
eggs = _temp.eggs
saus = _temp.sausage
I think this is the key. If we infer that from mod import * results in something like:
_temp = __import__('mod', globals(), locals(), [], 0)
printv = _temp.printv
foo = _temp.foo
This shows what the problem is. printv is a reference to the old version of printv; what mod.printv was pointing to at the time of import. Reassigning what the printv in mod is pointing to doesn't effect anything in mwe, because the mwe reference to printv is still looking at the previous lambda.
It's similar to how this doesn't change b:
a = 1
b = a
a = 2
b is still pointing to 1, because reassigning a doesn't effect what b is looking at.
On the other hand, mod.printv does work because we are now using a direct reference to the global in mod instead of a reference that points to printv in mod.
This was a random stab because I think I know the answer based on some random reading I did awhile ago. If I'm incorrect, please let me know and I'll remove this to avoid confusion.
I am currently trying to ship some bigger project. That is the reason why I decided to use submodules. Take a look at the project structure:
/sandbox
__init.py__
constants.py
/sub1
__init__.py
foo.py
In my constants.py file, I have declared a single global variable:
MYGLOBAL = 42
I want to use its value in foo.py. This file has 2 functions for testing:
def foofunc():
return 'I am foo.'
def constfunc():
return 'I am {MYGLOBAL}.'
Also, I put this code into /sandbox/sub1/__init__.py:
from .foo import *
from sandbox.constants import *
Now, when I use my interpreter, I try to use both functions like this:
>> import sandbox.sub1
>> sandbox.sub1.foofunc()
'I am foo.'
>> sandbox.sub1.MYGLOBAL
42
>> sandbox.sub1.constfunc()
NameError: name 'MYGLOBAL' is not defined
To my understanding, the global variable is in the same namespace as the functions, but somehow the function does not see it.
How can I access it? Python version is 3.6.
Thanks!
"Global" variables are not really global in Python, and are only available in the namespace of the modules in which they're defined.
You should import constants in foo.py so that MYGLOBAL can be made available as an attribute of the constants module object:
from sandbox import constants
def constfunc():
return f'I am {constants.MYGLOBAL}.'
A late answer to this very nice question (upvoted).
Here is one thing that will give you a better understanding of what is going on:
>>> sandbox.sub1.constfunc.__module__
'sandbox.sub1.foo'
>>> sandbox.sub1.constfunc.__globals__['MYGLOBAL']
KeyError: 'MYGLOBAL'
>>> sandbox.sub1.foo.MYGLOBAL = 5
>>> sandbox.sub1.constfunc.__globals__['MYGLOBAL']
5
>>> sandbox.sub1.constfunc()
'I am 5'
In other words,
"which module does this function actually belong in, and thus looks at to decide what module-global variables are available to it?"
You erroneously assumed it would be sandbox.sub1, but it's actually sandbox.sub1.foo. The reason is because the function's "parent module" is carried with the function object, even when this function object is copied to another module's "workspace".
With this understanding, there are several things you can do. One is as per the other answer here. An alternative solution might be to do this in __init__.py:
from . import foo
from .. import constants
foo.MYGLOBAL = constants.MYGLOBAL
etc.
Interesting things to note:
One thing to note is that the from X import Y as Z syntax is effectively equivalent to saying Z = X.Y. The reason this is an important insight is because it helps you realise that Z is not a 'reference' per se: if you change X.Y, Z will not automatically get updated.
If you change the Y in X to another value, and you want to update Z, you need to reimport it with the "from X import Y as Z" syntax.
Interestingly, you cannot simply do:
sandbox.sub1.constfunc.__module__ = sandbox.constants
and simply expect to 'replace' the function's module-global variables to those of sandbox.constants. There reason for this seems to be that the constfunc.__module__ attribute is simply a 'copy' of the original parent module, and changing it does not affect the actual parent module bound to the function (i.e., it does not dynamically 'rebind' the function to another module; though, since this is an internal variable, it may change in future python versions. Who knows. Don't rely on it.).
Also, the __module__ attribute is not used to create the constfunc.__globals__ dictionary for the function; this always seems to reflect the global dictionary of the 'parent module' directly. So simply replacing the sandbox.sub1.constfunc.__module__ variable doesn't automatically replace the constfunc.__globals__ dictionary, but updating sandbox.sub1.foo does.
I have two specific situations where I don't understand how importing works in Python:
1st specific situation:
When I import the same module in two different Python scripts, the module isn't imported twice, right? The first time Python encounters it, it is imported, and second time, does it check if the module has been imported, or does it make a copy?
2nd specific situation:
Consider the following module, called bla.py:
a = 10
And then, we have foo.py, a module which imports bla.py:
from bla import *
def Stuff ():
return a
And after that, we have a script called bar.py, which gets executed by the user:
from foo import *
Stuff() #This should return 10
a = 5
Stuff()
Here I don't know: Does Stuff() return 10 or 5?
Part 1
The module is only loaded once, so there is no performance loss by importing it again. If you actually wanted it to be loaded/parsed again, you'd have to reload() the module.
The first place checked is sys.modules, the cache of all modules that have been imported previously. [source]
Part 2
from foo import * imports a to the local scope. When assigning a value to a, it is replaced with the new value - but the original foo.a variable is not touched.
So unless you import foo and modify foo.a, both calls will return the same value.
For a mutable type such as a list or dict it would be different, modifying it would indeed affect the original variable - but assigning a new value to it would still not modify foo.whatever.
If you want some more detailed information, have a look at http://docs.python.org/reference/executionmodel.html:
The following constructs bind names: formal parameters to functions, import statements, class and function definitions (these bind the class or function name in the defining block), and targets that are identifiers if occurring in an assignment, for loop header, in the second position of an except clause header or after as in a with statement.
The two bold sections are the relevant ones for you: First the name a is bound to the value of foo.a during the import. Then, when doing a = 5, the name a is bound to 5. Since modifying a list/dict does not cause any binding, those operations would modify the original one (b and foo.b are bound to the same object on which you operate). Assigning a new object to b would be a binding operation again and thus separate b from foo.b.
It is also worth noting what exactly the import statement does:
import foo binds the module name to the module object in the current scope, so if you modify foo.whatever, you will work with the name in that module - any modifications/assignments will affect the variable in the module.
from foo import bar binds the given name(s) only (i.e. foo will remain unbound) to the element with the same name in foo - so operations on bar behave like explained earlier.
from foo import * behaves like the previous one, but it imports all global names which are not prefixed with an underscore. If the module defines __all__ only names inside this sequence are imported.
Part 3 (which doesn't even exist in your question :p)
The python documentation is extremely good and usually verbose - you find answer on almost every possible language-related question in there. Here are some useful links:
http://docs.python.org/reference/datamodel.html (classes, properties, magic methods, etc.) ()
http://docs.python.org/reference/executionmodel.html (how variables work in python)
http://docs.python.org/reference/expressions.html
http://docs.python.org/reference/simple_stmts.html (statements such as import, yield)
http://docs.python.org/reference/compound_stmts.html (block statements such as for, try, with)
To answer your first question:
No, python does not get 'imported' twice. When python loads a module, it checks for the module in sys.modules. If it is not in there, it is put in there, and loaded.
To answer your second question:
Modules can define what names they will export to a from camelot import * scenario, and the behavior is to create names for the existing values, not to reference existing variables (python does not have references).
On a somewhat related topic, doing a from camelot import * is not the same as a regular import.