We Keep Coding

Function properly defined and working but not printing results in terminal

I recently wrote a simple function that gets the present value of an asset, the function PV works properly and I have tested it. The function prints well in my pv file. However, when I run this code in the main file it does not print the output to the terminal and just closes after taking the inputs. Is there a reason for this? For reference the functions just perform some simple mathematics problems.
Below is a minimal reproduction, both files are in the same folder.
in file_x you have a function that works like this:
def func(w,x,y,z):
z1 = w/(1 + (x/100))**(y*z)
print(z1)
return 0
this function is then imported into another file which is written like so
from file_x import func
w = int(input("Future Value: "))
x = float(input("ROR: "))
y = float(input("# of periods: "))
z = float(input("# of payment per anum: "))
func(w,x,y,z)
My problem is that when I run the 2nd file it takes the inputs properly but does not print the result from the function. Hope it was explained properly

In interactive mode an unused expression (here PV(w,x,y,z) which does returns a value and that value is not used) is printed to the terminal, because it is a common way to just display expression values.
But when you run a script in a non interactive mode (python script.py) then those unused expression are just discarded. As a general rule, you should never have such an unused expression in a script, because the behaviour will depend on the interactive mode of the interpretor.
So you should be explicit:
_ = PV(w,x,y,z) # value will be discarded even in interactive mode
or
print(PV(w,x,y,z)) # value will be printed even in non interactive mode

How can i accept and run user's code securely on my web app?

I am working on a django based web app that takes python file as input which contains some function, then in backend i have some lists that are passed as parameters through the user's function,which will generate a single value output.The result generated will be used for some further computation.
Here is how the function inside the user's file look like :
def somefunctionname(list):
''' some computation performed on list'''
return float value
At present the approach that i am using is taking user's file as normal file input. Then in my views.py i am executing the file as module and passing the parameters with eval function. Snippet is given below.
Here modulename is the python file name that i had taken from user and importing as module
exec("import "+modulename)
result = eval(f"{modulename}.{somefunctionname}(arguments)")
Which is working absolutely fine. But i know this is not the secured approach.
My question , Is there any other way through which i can run users file securely as the method that i am using is not secure ? I know the proposed solutions can't be full proof but what are the other ways in which i can run this (like if it can be solved with dockerization then what will be the approach or some external tools that i can use with API )?
Or if possible can somebody tell me how can i simply sandbox this or any tutorial that can help me..?
Any reference or resource will be helpful.

It is an important question. In python sandboxing is not trivial.
It is one of the few cases where the question which version of python interpreter you are using. For example, Jyton generates Java bytecode, and JVM has its own mechanism to run code securely.
For CPython, the default interpreter, originally there were some attempts to make a restricted execution mode, that were abandoned long time ago.
Currently, there is that unofficial project, RestrictedPython that might give you what you need. It is not a full sandbox, i.e. will not give you restricted filesystem access or something, but for you needs it may be just enough.
Basically the guys there just rewrote the python compilation in a more restricted way.
What it allows to do is to compile a piece of code and then execute, all in a restricted mode. For example:
from RestrictedPython import safe_builtins, compile_restricted
source_code = """
print('Hello world, but secure')
"""
byte_code = compile_restricted(
source_code,
filename='<string>',
mode='exec'
)
exec(byte_code, {__builtins__ = safe_builtins})
>>> Hello world, but secure
Running with builtins = safe_builtins disables the dangerous functions like open file, import or whatever. There are also other variations of builtins and other options, take some time to read the docs, they are pretty good.
EDIT:
Here is an example for you use case
from RestrictedPython import safe_builtins, compile_restricted
from RestrictedPython.Eval import default_guarded_getitem
def execute_user_code(user_code, user_func, *args, **kwargs):
""" Executed user code in restricted env
Args:
user_code(str) - String containing the unsafe code
user_func(str) - Function inside user_code to execute and return value
*args, **kwargs - arguments passed to the user function
Return:
Return value of the user_func
"""
def _apply(f, *a, **kw):
return f(*a, **kw)
try:
# This is the variables we allow user code to see. #result will contain return value.
restricted_locals = {
"result": None,
"args": args,
"kwargs": kwargs,
}
# If you want the user to be able to use some of your functions inside his code,
# you should add this function to this dictionary.
# By default many standard actions are disabled. Here I add _apply_ to be able to access
# args and kwargs and _getitem_ to be able to use arrays. Just think before you add
# something else. I am not saying you shouldn't do it. You should understand what you
# are doing thats all.
restricted_globals = {
"__builtins__": safe_builtins,
"_getitem_": default_guarded_getitem,
"_apply_": _apply,
}
# Add another line to user code that executes #user_func
user_code += "\nresult = {0}(*args, **kwargs)".format(user_func)
# Compile the user code
byte_code = compile_restricted(user_code, filename="<user_code>", mode="exec")
# Run it
exec(byte_code, restricted_globals, restricted_locals)
# User code has modified result inside restricted_locals. Return it.
return restricted_locals["result"]
except SyntaxError as e:
# Do whaever you want if the user has code that does not compile
raise
except Exception as e:
# The code did something that is not allowed. Add some nasty punishment to the user here.
raise
Now you have a function execute_user_code, that receives some unsafe code as a string, a name of a function from this code, arguments, and returns the return value of the function with the given arguments.
Here is a very stupid example of some user code:
example = """
def test(x, name="Johny"):
return name + " likes " + str(x*x)
"""
# Lets see how this works
print(execute_user_code(example, "test", 5))
# Result: Johny likes 25
But here is what happens when the user code tries to do something unsafe:
malicious_example = """
import sys
print("Now I have the access to your system, muhahahaha")
"""
# Lets see how this works
print(execute_user_code(malicious_example, "test", 5))
# Result - evil plan failed:
# Traceback (most recent call last):
# File "restr.py", line 69, in <module>
# print(execute_user_code(malitious_example, "test", 5))
# File "restr.py", line 45, in execute_user_code
# exec(byte_code, restricted_globals, restricted_locals)
# File "<user_code>", line 2, in <module>
#ImportError: __import__ not found
Possible extension:
Pay attention that the user code is compiled on each call to the function. However, it is possible that you would like to compile the user code once, then execute it with different parameters. So all you have to do is to save the byte_code somewhere, then to call exec with a different set of restricted_locals each time.
EDIT2:
If you want to use import, you can write your own import function that allows to use only modules that you consider safe. Example:
def _import(name, globals=None, locals=None, fromlist=(), level=0):
safe_modules = ["math"]
if name in safe_modules:
globals[name] = __import__(name, globals, locals, fromlist, level)
else:
raise Exception("Don't you even think about it {0}".format(name))
safe_builtins['__import__'] = _import # Must be a part of builtins
restricted_globals = {
"__builtins__": safe_builtins,
"_getitem_": default_guarded_getitem,
"_apply_": _apply,
}
....
i_example = """
import math
def myceil(x):
return math.ceil(x)
"""
print(execute_user_code(i_example, "myceil", 1.5))
Note that this sample import function is VERY primitive, it will not work with stuff like from x import y. You can look here for a more complex implementation.
EDIT3
Note, that lots of python built in functionality is not available out of the box in RestrictedPython, it does not mean it is not available at all. You may need to implement some function for it to become available.
Even some obvious things like sum or += operator are not obvious in the restricted environment.
For example, the for loop uses _getiter_ function that you must implement and provide yourself (in globals). Since you want to avoid infinite loops, you may want to put some limits on the number of iterations allowed. Here is a sample implementation that limits number of iterations to 100:
MAX_ITER_LEN = 100
class MaxCountIter:
def __init__(self, dataset, max_count):
self.i = iter(dataset)
self.left = max_count
def __iter__(self):
return self
def __next__(self):
if self.left > 0:
self.left -= 1
return next(self.i)
else:
raise StopIteration()
def _getiter(ob):
return MaxCountIter(ob, MAX_ITER_LEN)
....
restricted_globals = {
"_getiter_": _getiter,
....
for_ex = """
def sum(x):
y = 0
for i in range(x):
y = y + i
return y
"""
print(execute_user_code(for_ex, "sum", 6))
If you don't want to limit loop count, just use identity function as _getiter_:
restricted_globals = {
"_getiter_": labmda x: x,
Note that simply limiting the loop count does not guarantee security. First, loops can be nested. Second, you cannot limit the execution count of a while loop. To make it secure, you have to execute unsafe code under some timeout.
Please take a moment to read the docs.
Note that not everything is documented (although many things are). You have to learn to read the project's source code for more advanced things. Best way to learn is to try and run some code, and to see what kind function is missing, then to see the source code of the project to understand how to implement it.
EDIT4
There is still another problem - restricted code may have infinite loops. To avoid it, some kind of timeout is required on the code.
Unfortunately, since you are using django, that is multi threaded unless you explicitly specify otherwise, simple trick for timeouts using signeals will not work here, you have to use multiprocessing.
Easiest way in my opinion - use this library. Simply add a decorator to execute_user_code so it will look like this:
#timeout_decorator.timeout(5, use_signals=False)
def execute_user_code(user_code, user_func, *args, **kwargs):
And you are done. The code will never run more than 5 seconds.
Pay attention to use_signals=False, without this it may have some unexpected behavior in django.
Also note that this is relatively heavy on resources (and I don't really see a way to overcome this). I mean not really crazy heavy, but it is an extra process spawn. You should hold that in mind in your web server configuration - the api which allows to execute arbitrary user code is more vulnerable to ddos.

For sure with docker you can sandbox the execution if you are careful. You can restrict CPU cycles, max memory, close all network ports, run as a user with read only access to the file system and all).
Still,this would be extremely complex to get it right I think. For me you shall not allow a client to execute arbitrar code like that.
I would be to check if a production/solution isn't already done and use that. I was thinking that some sites allow you to submit some code (python, java, whatever) that is executed on the server.

Writing python unit tests inside the actual code

Sometimes I'm writing small utilities functions and pack them as python package.
How small? 30 - 60 lines of python.
And my question is do you think writing the tests inside the actual code is bad? abusing?
I can see a great benefits like usage examples inside the code itself without jumping between files (again from really small projects).
Example:
#!/usr/bin/env python
# Actual code
def increment(number, by=1):
return number += by
# Tests
def test_increment_positive():
assert increment(1) == 2
def test_increment_negative():
assert increment(-5) == -4
def test_increment_zero():
assert increment(0) == 1
The general Idea taken from the monitoring framework riemann which I use, in riemann you write your tests file along with your code link

You can write doctests inside your documentation to indicate how your function should be used:
def increment(number, by=1):
""" Increments the given number by some other number
>>> increment(3)
4
>>> increment(5,3)
8
"""
return number += by
From the documentation:
To check that a module’s docstrings are up-to-date by verifying that all interactive examples still work as documented.
To perform regression testing by verifying that interactive examples from a test file or a test object work as expected.
To write tutorial documentation for a package, liberally illustrated with input-output examples. Depending on whether the
examples or the expository text are emphasized, this has the
flavor of “literate testing” or “executable documentation”

Parallel mapping functions in IPython w/ multiple parameters

I'm trying to use IPython's parallel environment and so far, it's looking great but I'm running into a problem. Lets say that I have a function, defined in a library
def func(a,b):
...
that I use when I want to evaluate on one value of a and a bunch of values of b.
[func(myA, b) for b in myLongList]
Obviously, the real function is more complicated but the essence of the matter is that it takes multiple parameters and I'd like to map over only one of them. The problem is that map, #dview.parallel, etc. map over all the arguments.
So lets say I want to get the answer to func(myA, myLongList). The obvious way to do this is to curry, either w/ functools.partial or just as
dview.map_sync(lambda b: func(myA, b), myLongList)
However, this does not work correctly on remote machines. The reason is that when the lambda expression is pickled, the value of myA is not included and instead, the value of myA from the local scope on the remote machine is used. When closures get pickled, the variables they close over don't.
Two ways I can think of doing this that will actually work are to manually construct lists for every argument and have map work over all of the arguments,
dview.map_sync(func, [myA]*len(myLongList), myLongList)
or to horrifically use the data as default arguments to a function, forcing it to get pickled:
# Can't use a lambda here b/c lambdas don't use default arguments :(
def parallelFunc(b, myA = myA):
return func(myA, b)
dview.map_sync(parallelFunc, myLongList)
Really, this all seems horribly contorted when the real function takes a lot of parameters and is more complicated. Is there some idiomatic way of doing this? Something like
#parallel(mapOver='b')
def bigLongFn(a, b):
...
but as far as I know, nothing like the 'mapOver' thing exists. I probably have an idea of how to implement it ... this just feels like a very basic operation that there should exist support for so I want to check if I'm missing something.

I can improve a bit on batu's answer (which I think is a good one, but doesn't perhaps document in as much detail WHY you use those options). The ipython documentation is also currently woefully inadequate on this point. So your function is of the form:
def myfxn(a,b,c,d):
....
return z
and stored in a file called mylib. Lets say b,c, and d are the same during your run, so you write a lambda function to reduce it to a 1-parameter function.
import mylib
mylamfxn=lambda a:mylib.myfxn(a,b,c,d)
and you want to run:
z=dview.map_sync(mylamfxn, iterable_of_a)
In a dream world, everything would magically work like that. However, first you'd get an error of "mylib not found," because the ipcluster processes haven't loaded mylib. Make sure the ipcluster processes have "mylib" in their python path and are in the correct working directory for myfxn, if necessary. Then you need to add to your python code:
dview.execute('import mylib')
which runs the import mylib command on each process. If you try again, you'll get an error along the lines of "global variable b not defined" because while the variables are in your python session, they aren't in the ipcluster processes. However, python provides a method of copying a group of variables to the subprocesses. Continuing the example above:
mydict=dict(b=b, c=c, d=d)
dview.push(mydict)
Now all of the subprocesses have access to b,c,and d. Then you can just run:
z=dview.map_sync(mylamfxn, iterable_of_a)
and it should now work as advertised. Anyway, I'm new to parallel computing with python, and found this thread useful, so I thought I'd try to help explain a few of the points that confused me a bit....
The final code would be:
import mylib
#set up parallel processes, start ipcluster from command line prior!
from IPython.parallel import Client
rc=Client()
dview=rc[:]
#...do stuff to get iterable_of_a and b,c,d....
mylamfxn=lambda a:mylib.myfxn(a,b,c,d)
dview.execute('import mylib')
mydict=dict(b=b, c=c, d=d)
dview.push(mydict)
z=dview.map_sync(mylamfxn, iterable_of_a)
This is probably the quickest and easiest way to make pretty much any embarrassingly parallel code run parallel in python....
UPDATE You can also use dview to push all the data without loops and then use an lview (i.e. lview=rc.load_balanced_view(); lview.map(...) to do the actual calculation in load balanced fashion.

This is my first message to StackOverflow so please be gentle ;) I was trying to do the same thing, and came up with the following. I am pretty sure this is not the most efficient way, but seems to work somewhat. One caveat for now is that for some reason I only see two engines working at 100%, the others are sitting almost idle...
In order to call a multiple arg function in map I first wrote this routine in my personal parallel.py module:
def map(r,func, args=None, modules=None):
"""
Before you run parallel.map, start your cluster (e.g. ipcluster start -n 4)
map(r,func, args=None, modules=None):
args=dict(arg0=arg0,...)
modules='numpy, scipy'
examples:
func= lambda x: numpy.random.rand()**2.
z=parallel.map(r_[0:1000], func, modules='numpy, numpy.random')
plot(z)
A=ones((1000,1000));
l=range(0,1000)
func=lambda x : A[x,l]**2.
z=parallel.map(r_[0:1000], func, dict(A=A, l=l))
z=array(z)
"""
from IPython.parallel import Client
mec = Client()
mec.clear()
lview=mec.load_balanced_view()
for k in mec.ids:
mec[k].activate()
if args is not None:
mec[k].push(args)
if modules is not None:
mec[k].execute('import '+modules)
z=lview.map(func, r)
out=z.get()
return out
As you can see the function takes an args parameter which is a dict of parameters in the head nodes workspace. These parameters are then pushed to the engines. At that point they become local objects and can be used in the function directly. For example in the last example given above in comments, the A matrix is sliced using the l engine-local variable.
I must say that even though the above function works, I am not 100% happy with it at the moment. If I can come up with something better, I will post it here.
UPDATE:2013/04/11
I made minor changes to the code:
- The activate statement was missing brackets. Causing it not to run.
- Moved mec.clear() to the top of the function, as opposed to the end.
I also noticed that it works best if I run it within ipython. For example, I may get errors if I run a script using the above function as "python ./myparallelrun.py" but not if I run it within ipython using "%run ./myparallelrun.py". Not sure why...

An elegant way to do this is with partial functions.
If you know that you want the first argument of foo to be myArg, you can create a new function bar by
from functools import partial
bar = partial(foo, myARg)
bar(otherArg) will then return foo(myArg,otherArg)

let's build on that:
dview.map_sync(func, [myA]*len(myLongList), myLongList)
maybe the following would work:
from itertools import izip_longest
dview.map_sync(func, izip_longest(myLongList, [], fillvalue=myA))
example:
>>> # notice that a is a tuple
... concat = lambda a: '%s %s' % a
>>> mylonglist = range(10)
>>> from itertools import izip_longest
>>> map(concat, izip_longest(mylonglist, [], fillvalue='mississippi'))
['0 mississippi', '1 mississippi', '2 mississippi', '3 mississippi',
'4 mississippi', '5 mississippi', '6 mississippi', '7 mississippi',
'8 mississippi', '9 mississippi']

I am posting Alex S. comment as answer. This probably is right approach for this problem:
Just do partial application with a lambda. I know it looks weird, but using my_f = lambda a,my=other,arguments=go,right=here : f(a,my,arguments,right) is the simplest way to go about it without falling into pickling and pushing problems.

How to emulate os.path.samefile behaviour on Windows and Python 2.7?

Given two paths I have to compare if they're pointing to the same file or not. In Unix this can be done with os.path.samefile, but as documentation states it's not available in Windows.
What's the best way to emulate this function?
It doesn't need to emulate common case. In my case there are the following simplifications:
Paths don't contain symbolic links.
Files are in the same local disk.
Now I use the following:
def samefile(path1, path2)
return os.path.normcase(os.path.normpath(path1)) == \
os.path.normcase(os.path.normpath(path2))
Is this OK?

According to issue#5985 the os.path.samefile and os.path.sameopenfile are now in py3k. I verified this on Python 3.3.0
For older versions of Python here's a way which uses the GetFileInformationByHandle function:
see_if_two_files_are_the_same_file

The os.stat system call returns a tuple with a lot of information about each file - including creation and last modification time stamps, size, file attributes. The chances of different files having the same paramters are very slim. I think it is very resonable to do:
def samefile(file1, file2):
return os.stat(file1) == os.stat(file2)

The real use-case of os.path.samefile is not symbolic links, but hard links. os.path.samefile(a, b) returns True if a and b are both hard links to the same file. They might not have the same path.

I know this is a late answer in this thread. But I use python on Windows, and ran into this issue today, found this thread, and found that os.path.samefile doesn't work for me.
So, to answer the OP, now to emulate os.path.samefile, this is how I emulate it:
# because some versions of python do not have os.path.samefile
# particularly, Windows. :(
#
def os_path_samefile(pathA, pathB):
statA = os.stat(pathA) if os.path.isfile(pathA) else None
if not statA:
return False
statB = os.stat(pathB) if os.path.isfile(pathB) else None
if not statB:
return False
return (statA.st_dev == statB.st_dev) and (statA.st_ino == statB.st_ino)
It is not as tight as possible, because I was more interested in being clear in what I was doing.
I tested this on Windows-10, using python 2.7.15.