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.
In my python package I have an entry_point run.py file which takes the seed (e.g. 42) and the cuda device (e.g. "cuda:0") as command line argument.
Since both of these variables are used throughout the entire package at different places, I don't want to pass them as arguments from function to function. Hence, I did the following:
utils.py:
import random
import numpy as np
import torch
def set_device(device: str):
global _DEVICE
_DEVICE = torch.device(device)
def get_device() -> torch.device:
return _DEVICE
def set_seed_number(seed: int):
global _SEED
_SEED = seed
def set_seeds():
torch.manual_seed(_SEED)
random.seed(_SEED)
np.random.seed(_SEED)
And then within run.py I set these variables once by calling:
from package.utils import set_device, set_seed_number
...
set_device(device)
set_seed_number(seed=seed)
Now I can import and call the get_device()and set_seeds method from anywhere in my package and I don't have to pass these variables as arguments.
So far this approach works fine, but after reading that using globals in python is strongly discouraged I am wondering if there is a more pythonic way of achieving the above discribed goal?
I already thought of having a dedicated Singleton class, which dynamically would instantiate those constants but I am not exactly sure if and how that would work and if it would be considered more "pythonic" after all.
Thanks already for your answers and maybe you can point me to some patterns that seem applicable in this situation. I can only guess that I am not the first one trying to achieve the above discribed goal.
I can't honestly see a problem with global if it is used sparingly and only when there is a strong reason to do so. (I think the strong discouragement aganst global is because it is often abused.)
But as regards your proposed custom class, there is no need to instantiate it -- you can just set class variables.
main.py
import settings
settings.set_foo(3)
print(settings.Settings.foo)
settings.py
class Settings:
pass
def set_foo(x):
Settings.foo = x
This is no different in principle from putting your data items inside some other mutable collection e.g. a dictionary and then setting them inside functions in the module that defines it (or another that imports it).
main.py
import settings
settings.set_foo(3)
print(settings.settings['foo'])
settings.py
settings = {}
def set_foo(x):
settings['foo'] = x
I am not too far into python yet and here is the case.
Say I have one python file called functions.py which holds a class with my functions. Below is an example.
import json
class Functionalities:
def addelement(element):
# code goes here`
And I have another python file, kind of 'executable' script which does all the job using functions from functions.py class
Adding from . import functions doesn't help.
How to I call functions from the class from another file?
Unlike java, you don't have to use classes in python. If a class is used only as a holder for functions, chances are that you want a free function (one without a class) instead.
But to answer your actual question, you can use the import statement. That will run the other .py file, and make the namespace available so you can call things defined there.
functions.py:
import json
class Functionalities:
def addelement(element):
# code goes here`
main.py:
import functions
f = functions.Functionalities()
f.addelement('some element')
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 want to test a function in python, but it relies on a module-level "private" function, that I don't want called, but I'm having trouble overriding/mocking it. Scenario:
module.py
_cmd(command, args):
# do something nasty
function_to_be_tested():
# do cool things
_cmd('rm', '-rf /')
return 1
test_module.py
import module
test_function():
assert module.function_to_be_tested() == 1
Ideally, in this test I dont want to call _cmd. I've looked at some other threads, and I've tried the following with no luck:
test_function():
def _cmd(command, args):
# do nothing
pass
module._cmd = _cmd
although checking module._cmd against _cmd doesn't give the correct reference. Using mock:
from mock import patch
def _cmd_mock(command, args):
# do nothing
pass
#patch('module._cmd', _cmd_mock)
test_function():
...
gives the correct reference when checking module._cmd, although `function_to_be_tested' still uses the original _cmd (as evidenced by it doing nasty things).
This is tricky because _cmd is a module-level function, and I dont want to move it into a module
[Disclaimer]
The synthetic example posted in this question works and the described issue become from specific implementation in production code. Maybe this question should be closed as off topic because the issue is not reproducible.
[Note] For impatient people Solution is at the end of the answer.
Anyway that question given to me a good point to thought: how we can patch a method reference when we cannot access to the variable where the reference is?
Lot of times I found some issue like this. There are lot of ways to meet that case and the commons are
Decorators: the instance we would like replace is passed as decorator argument or used in decorator static implementation
What we would like to patch is a default argument of a method
In both cases maybe refactor the code is the best way to play with that but what about if we are playing with some legacy code or the decorator is a third part decorator?
Ok, we have the back on the wall but we are using python and in python nothing is impossible. What we need is just the reference of the function/method to patch and instead of patching its reference we can patch the __code__: yes I'm speaking about patching the bytecode instead the function.
Get a real example. I'm using default parameter case that is simple, but it works either in decorator case.
def cmd(a):
print("ORIG {}".format(a))
def cmd_fake(a):
print("NEW {}".format(a))
def do_work(a, c=cmd):
c(a)
do_work("a")
cmd=cmd_fake
do_work("b")
Output:
ORIG a
ORIG b
Ok In this case we can test do_work by passing cmd_fake but there some cases where is impossible do it: for instance what about if we need to call something like that:
def what_the_hell():
list(map(lambda a:do_work(a), ["c","d"]))
what we can do is patch cmd.__code__ instead of _cmd by
cmd.__code__ = cmd_fake.__code__
So follow code
do_work("a")
what_the_hell()
cmd.__code__ = cmd_fake.__code__
do_work("b")
what_the_hell()
Give follow output:
ORIG a
ORIG c
ORIG d
NEW b
NEW c
NEW d
Moreover if we want to use a mock we can do it by add follow lines:
from unittest.mock import Mock, call
cmd_mock = Mock()
def cmd_mocker(a):
cmd_mock(a)
cmd.__code__=cmd_mocker.__code__
what_the_hell()
cmd_mock.assert_has_calls([call("c"),call("d")])
print("WORKS")
That print out
WORKS
Maybe I'm done... but OP still wait for a solution of his issue
from mock import patch, Mock
cmd_mock = Mock()
#A closure for grabbing the right function code
def cmd_mocker(a):
cmd_mock(a)
#patch.object(module._cmd,'__code__', new=cmd_mocker.__code__)
test_function():
...
Now I should say never use this trick unless you are with the back on the wall. Test should be simple to understand and to debug ... try to debug something like this and you will become mad!