Related
Note: This question is for informational purposes only. I am interested to see how deep into Python's internals it is possible to go with this.
Not very long ago, a discussion began inside a certain question regarding whether the strings passed to print statements could be modified after/during the call to print has been made. For example, consider the function:
def print_something():
print('This cat was scared.')
Now, when print is run, then the output to the terminal should display:
This dog was scared.
Notice the word "cat" has been replaced by the word "dog". Something somewhere somehow was able to modify those internal buffers to change what was printed. Assume this is done without the original code author's explicit permission (hence, hacking/hijacking).
This comment from the wise #abarnert, in particular, got me thinking:
There are a couple of ways to do that, but they're all very ugly, and
should never be done. The least ugly way is to probably replace the
code object inside the function with one with a different co_consts
list. Next is probably reaching into the C API to access the str's
internal buffer. [...]
So, it looks like this is actually possible.
Here's my naive way of approaching this problem:
>>> import inspect
>>> exec(inspect.getsource(print_something).replace('cat', 'dog'))
>>> print_something()
This dog was scared.
Of course, exec is bad, but that doesn't really answer the question, because it does not actually modify anything during when/after print is called.
How would it be done as #abarnert has explained it?
First, there's actually a much less hacky way. All we want to do is change what print prints, right?
_print = print
def print(*args, **kw):
args = (arg.replace('cat', 'dog') if isinstance(arg, str) else arg
for arg in args)
_print(*args, **kw)
Or, similarly, you can monkeypatch sys.stdout instead of print.
Also, nothing wrong with the exec … getsource … idea. Well, of course there's plenty wrong with it, but less than what follows here…
But if you do want to modify the function object's code constants, we can do that.
If you really want to play around with code objects for real, you should use a library like bytecode (when it's finished) or byteplay (until then, or for older Python versions) instead of doing it manually. Even for something this trivial, the CodeType initializer is a pain; if you actually need to do stuff like fixing up lnotab, only a lunatic would do that manually.
Also, it goes without saying that not all Python implementations use CPython-style code objects. This code will work in CPython 3.7, and probably all versions back to at least 2.2 with a few minor changes (and not the code-hacking stuff, but things like generator expressions), but it won't work with any version of IronPython.
import types
def print_function():
print ("This cat was scared.")
def main():
# A function object is a wrapper around a code object, with
# a bit of extra stuff like default values and closure cells.
# See inspect module docs for more details.
co = print_function.__code__
# A code object is a wrapper around a string of bytecode, with a
# whole bunch of extra stuff, including a list of constants used
# by that bytecode. Again see inspect module docs. Anyway, inside
# the bytecode for string (which you can read by typing
# dis.dis(string) in your REPL), there's going to be an
# instruction like LOAD_CONST 1 to load the string literal onto
# the stack to pass to the print function, and that works by just
# reading co.co_consts[1]. So, that's what we want to change.
consts = tuple(c.replace("cat", "dog") if isinstance(c, str) else c
for c in co.co_consts)
# Unfortunately, code objects are immutable, so we have to create
# a new one, copying over everything except for co_consts, which
# we'll replace. And the initializer has a zillion parameters.
# Try help(types.CodeType) at the REPL to see the whole list.
co = types.CodeType(
co.co_argcount, co.co_kwonlyargcount, co.co_nlocals,
co.co_stacksize, co.co_flags, co.co_code,
consts, co.co_names, co.co_varnames, co.co_filename,
co.co_name, co.co_firstlineno, co.co_lnotab,
co.co_freevars, co.co_cellvars)
print_function.__code__ = co
print_function()
main()
What could go wrong with hacking up code objects? Mostly just segfaults, RuntimeErrors that eat up the whole stack, more normal RuntimeErrors that can be handled, or garbage values that will probably just raise a TypeError or AttributeError when you try to use them. For examples, try creating a code object with just a RETURN_VALUE with nothing on the stack (bytecode b'S\0' for 3.6+, b'S' before), or with an empty tuple for co_consts when there's a LOAD_CONST 0 in the bytecode, or with varnames decremented by 1 so the highest LOAD_FAST actually loads a freevar/cellvar cell. For some real fun, if you get the lnotab wrong enough, your code will only segfault when run in the debugger.
Using bytecode or byteplay won't protect you from all of those problems, but they do have some basic sanity checks, and nice helpers that let you do things like insert a chunk of code and let it worry about updating all offsets and labels so you can't get it wrong, and so on. (Plus, they keep you from having to type in that ridiculous 6-line constructor, and having to debug the silly typos that come from doing so.)
Now on to #2.
I mentioned that code objects are immutable. And of course the consts are a tuple, so we can't change that directly. And the thing in the const tuple is a string, which we also can't change directly. That's why I had to build a new string to build a new tuple to build a new code object.
But what if you could change a string directly?
Well, deep enough under the covers, everything is just a pointer to some C data, right? If you're using CPython, there's a C API to access the objects, and you can use ctypes to access that API from within Python itself, which is such a terrible idea that they put a pythonapi right there in the stdlib's ctypes module. :) The most important trick you need to know is that id(x) is the actual pointer to x in memory (as an int).
Unfortunately, the C API for strings won't let us safely get at the internal storage of an already-frozen string. So screw safely, let's just read the header files and find that storage ourselves.
If you're using CPython 3.4 - 3.7 (it's different for older versions, and who knows for the future), a string literal from a module that's made of pure ASCII is going to be stored using the compact ASCII format, which means the struct ends early and the buffer of ASCII bytes follows immediately in memory. This will break (as in probably segfault) if you put a non-ASCII character in the string, or certain kinds of non-literal strings, but you can read up on the other 4 ways to access the buffer for different kinds of strings.
To make things slightly easier, I'm using the superhackyinternals project off my GitHub. (It's intentionally not pip-installable because you really shouldn't be using this except to experiment with your local build of the interpreter and the like.)
import ctypes
import internals # https://github.com/abarnert/superhackyinternals/blob/master/internals.py
def print_function():
print ("This cat was scared.")
def main():
for c in print_function.__code__.co_consts:
if isinstance(c, str):
idx = c.find('cat')
if idx != -1:
# Too much to explain here; just guess and learn to
# love the segfaults...
p = internals.PyUnicodeObject.from_address(id(c))
assert p.compact and p.ascii
addr = id(c) + internals.PyUnicodeObject.utf8_length.offset
buf = (ctypes.c_int8 * 3).from_address(addr + idx)
buf[:3] = b'dog'
print_function()
main()
If you want to play with this stuff, int is a whole lot simpler under the covers than str. And it's a lot easier to guess what you can break by changing the value of 2 to 1, right? Actually, forget imagining, let's just do it (using the types from superhackyinternals again):
>>> n = 2
>>> pn = PyLongObject.from_address(id(n))
>>> pn.ob_digit[0]
2
>>> pn.ob_digit[0] = 1
>>> 2
1
>>> n * 3
3
>>> i = 10
>>> while i < 40:
... i *= 2
... print(i)
10
10
10
… pretend that code box has an infinite-length scrollbar.
I tried the same thing in IPython, and the first time I tried to evaluate 2 at the prompt, it went into some kind of uninterruptable infinite loop. Presumably it's using the number 2 for something in its REPL loop, while the stock interpreter isn't?
Monkey-patch print
print is a builtin function so it will use the print function defined in the builtins module (or __builtin__ in Python 2). So whenever you want to modify or change the behavior of a builtin function you can simply reassign the name in that module.
This process is called monkey-patching.
# Store the real print function in another variable otherwise
# it will be inaccessible after being modified.
_print = print
# Actual implementation of the new print
def custom_print(*args, **options):
_print('custom print called')
_print(*args, **options)
# Change the print function globally
import builtins
builtins.print = custom_print
After that every print call will go through custom_print, even if the print is in an external module.
However you don't really want to print additional text, you want to change the text that is printed. One way to go about that is to replace it in the string that would be printed:
_print = print
def custom_print(*args, **options):
# Get the desired seperator or the default whitspace
sep = options.pop('sep', ' ')
# Create the final string
printed_string = sep.join(args)
# Modify the final string
printed_string = printed_string.replace('cat', 'dog')
# Call the default print function
_print(printed_string, **options)
import builtins
builtins.print = custom_print
And indeed if you run:
>>> def print_something():
... print('This cat was scared.')
>>> print_something()
This dog was scared.
Or if you write that to a file:
test_file.py
def print_something():
print('This cat was scared.')
print_something()
and import it:
>>> import test_file
This dog was scared.
>>> test_file.print_something()
This dog was scared.
So it really works as intended.
However, in case you only temporarily want to monkey-patch print you could wrap this in a context-manager:
import builtins
class ChangePrint(object):
def __init__(self):
self.old_print = print
def __enter__(self):
def custom_print(*args, **options):
# Get the desired seperator or the default whitspace
sep = options.pop('sep', ' ')
# Create the final string
printed_string = sep.join(args)
# Modify the final string
printed_string = printed_string.replace('cat', 'dog')
# Call the default print function
self.old_print(printed_string, **options)
builtins.print = custom_print
def __exit__(self, *args, **kwargs):
builtins.print = self.old_print
So when you run that it depends on the context what is printed:
>>> with ChangePrint() as x:
... test_file.print_something()
...
This dog was scared.
>>> test_file.print_something()
This cat was scared.
So that's how you could "hack" print by monkey-patching.
Modify the target instead of the print
If you look at the signature of print you'll notice a file argument which is sys.stdout by default. Note that this is a dynamic default argument (it really looks up sys.stdout every time you call print) and not like normal default arguments in Python. So if you change sys.stdout print will actually print to the different target even more convenient that Python also provides a redirect_stdout function (from Python 3.4 on, but it's easy to create an equivalent function for earlier Python versions).
The downside is that it won't work for print statements that don't print to sys.stdout and that creating your own stdout isn't really straightforward.
import io
import sys
class CustomStdout(object):
def __init__(self, *args, **kwargs):
self.current_stdout = sys.stdout
def write(self, string):
self.current_stdout.write(string.replace('cat', 'dog'))
However this also works:
>>> import contextlib
>>> with contextlib.redirect_stdout(CustomStdout()):
... test_file.print_something()
...
This dog was scared.
>>> test_file.print_something()
This cat was scared.
Summary
Some of these points have already be mentioned by #abarnet but I wanted to explore these options in more detail. Especially how to modify it across modules (using builtins/__builtin__) and how to make that change only temporary (using contextmanagers).
A simple way to capture all output from a print function and then process it, is to change the output stream to something else, e.g. a file.
I'll use a PHP naming conventions (ob_start, ob_get_contents,...)
from functools import partial
output_buffer = None
print_orig = print
def ob_start(fname="print.txt"):
global print
global output_buffer
print = partial(print_orig, file=output_buffer)
output_buffer = open(fname, 'w')
def ob_end():
global output_buffer
close(output_buffer)
print = print_orig
def ob_get_contents(fname="print.txt"):
return open(fname, 'r').read()
Usage:
print ("Hi John")
ob_start()
print ("Hi John")
ob_end()
print (ob_get_contents().replace("Hi", "Bye"))
Would print
Hi John
Bye John
Let's combine this with frame introspection!
import sys
_print = print
def print(*args, **kw):
frame = sys._getframe(1)
_print(frame.f_code.co_name)
_print(*args, **kw)
def greetly(name, greeting = "Hi")
print(f"{greeting}, {name}!")
class Greeter:
def __init__(self, greeting = "Hi"):
self.greeting = greeting
def greet(self, name):
print(f"{self.greeting}, {name}!")
You'll find this trick prefaces every greeting with the calling function or method. This might be very useful for logging or debugging; especially as it lets you "hijack" print statements in third party code.
I am able to successfully mock a function, and I am sure that the original is not called. I added a huge print statement to the original function and when I mock it, this print is not called. When I turn the mock back on, the print statement is not called.
However, my assert_called is failing saying it was never called. Has anyone ever experienced something like this?
class FooTestCase(unittest.TestCase):
#mock.patch('MyObj.helper_function')
def test_simple(self, mock_hf):
my_obj = MyObj()
# internally, this class imports HelperModule
# and the method calls helper_function
my_obj.do_something()
mock_hf.helper_function.assert_called()
return
My error response
AssertionError: Expected 'helper_function' to have been called.
Update
I just added the following lines right before the assertion
print mock_cw.method_calls
print mock_cw.mock_calls
method_calls is an empty list, while mock_calls is a list with 1 item which is
[call(arg1_expected_for_helper_fn, arg2_expected_for_helper_fn)]
Yet the assert still fails
Usually an error like this is a result of not patching the correct location. Try to patch the object itself with this:
#patch.object(MyObj, "helper_function")
def test_simple(mock_hf):
...
Since MyObj is (assumed to be) imported at the top of the test file, this patches the method on that object directly.
The issue is that I was checking to see if mock_hf.helper_function was called, but mock_hf is already mapped to the helper_function. I was more or less checking that helper_function.helper_function was called rather than just helper_function.
The assert line needed to be
mock_hf.assert_called()
I see the original poster has done this, but for anyone else stumbling on this as I did...
Don't forget you need to wrap your expected calls in a call object e.g.
mock_logger.assert_has_calls([call(expected_log_message_1), call(expected_log_message_2)])
If you don't do that, it will complain that the expected call did not happen and you will spend ages comparing the output to try and work out why (as I did!).
I am trying to make a function's output behave as if it's my input. The goal is to make a new output from the old output.
I have some code that looks like this:
def func():
BLOCK OF CODE
func()
There is no return statement in the function and no parameters within the parenthesis.
When I type func() to call my function as shown above, I get the desired output, which is a bunch of printed statements. Now I want to do something with that output to get another output.
All I'm trying to do is effectively "pipe" the output of one function into the input of another function (or, if possible, not even worry about creating another function at all, and instead doing something more direct). I looked into Python 3 writing to a pipe
but it did not help me. I also tried defining another function and using the preceding function as a parameter, which did not work either:
def another_func(func):
print another_statement
another_func(func)
I also tried making a closure (which "kind" of worked because at least it printed the same thing that func() would print, but still not very encouraging):
def func():
def another_func():
print another_statement
BLOCK OF CODE
another_func()
Finally, I tried designing both a decorator and a nested function to accomplish this, but I have no parameters in my function, which really threw off my code (didn't print anything at all).
Any advice on how to manipulate a function's output like as if it is your input so that it's possible to create a new output?
You could achieve this by redirecting stdout using a decorator:
from StringIO import StringIO
import sys
def pipe(f):
def decorated(*args, **kwargs):
old,sys.stdout = sys.stdout,StringIO()
try:
result = f(*args, **kwargs)
output = sys.stdout.getvalue()
finally:
sys.stdout = old
return result, output
return decorated
You could then get the result, output pair from any decorated function, eg:
#pipe
def test(x):
print x
return 0
test(3) -> (0, '3\n')
However, I can't think of a good reason why you'd want to do this.
(Actually, that's not quite true; it is handy when writing unit tests for user IO, such as when testing student assignments in a software engineering course. I seriously doubt that that's what the OP is trying to do, though.)
Return the desired value(s) from the function - instead of printing the values on the console, return them as strings, numbers, lists or any other type that makes sense. Otherwise, how do you expect to "connect" the output of a function as the input to another, if there is no output to begin with?
Of course, printing on the console doesn't count as output unless you're planning to eventually use OS pipes or a similar mechanism to connect two programs on the console, but keep things simple! just use the function's return values and worry about pipes later if and only if that's necessary for your problem in particular.
After reading the comments: "connecting" two functions by printing on the console from one and reading from the console from the other would be a really bad idea in this case, first you have to grasp the way functions return values to each other, trust me on this one: you have to rethink your program! even though other answers (strictly speaking) answer your original question, that's absolutely not what you should do.
just for fun ... because OP asked for it
import StringIO
import sys
def func1():
for i in range(1,10):
print "some stuff %d"%i
def func2(func):
old_std = sys.stdout
sys.stdout = StringIO.StringIO()
try:
func()
return sys.stdout.getvalue().splitlines()
finally:
sys.stdout = old_std
print func2(func1)
You need to return a value from your function. This can be used to assign the value into another variable.
Say I define some function doubleThis that will double the input
def doubleThis(x):
print 'this is x :', x
return x * 2 # note the return keyword
Now I can call the function with 3, and it returns 6 as expected
>>> doubleThis(3)
this is x : 3
6
Now I have another function subtractOne that returns the input value, minus 1.
def subtractOne(i):
print 'this is i :', i
return i - 1
Now comes the answer to your question. Note that we can call the first function as the input to the second, due to the fact that it has a return value.
>>> subtractOne(doubleThis(3))
this is x : 3
this is i : 6
5
I'm struggling with this using timeit and was wondering if anyone had any tips
Basically I have a function(that I pass a value to) that I want to test the speed of and created this:
if __name__=='__main__':
from timeit import Timer
t = Timer(superMegaIntenseFunction(10))
print t.timeit(number=1)
but when I run it, I get weird errors like coming from the timeit module.:
ValueError: stmt is neither a string nor callable
If I run the function on its own, it works fine. Its when I wrap it in the time it module, I get the errors(I have tried using double quotes and without..sameoutput).
any suggestions would be awesome!
Thanks!
Make it a callable:
if __name__=='__main__':
from timeit import Timer
t = Timer(lambda: superMegaIntenseFunction(10))
print(t.timeit(number=1))
Should work
Timer(superMegaIntenseFunction(10)) means "call superMegaIntenseFunction(10), then pass the result to Timer". That's clearly not what you want. Timer expects either a callable (just as it sounds: something that can be called, such as a function), or a string (so that it can interpret the contents of the string as Python code). Timer works by calling the callable-thing repeatedly and seeing how much time is taken.
Timer(superMegaIntenseFunction) would pass the type check, because superMegaIntenseFunction is callable. However, Timer wouldn't know what values to pass to superMegaIntenseFunction.
The simple way around this, of course, is to use a string with the code. We need to pass a 'setup' argument to the code, because the string is "interpreted as code" in a fresh context - it doesn't have access to the same globals, so you need to run another bit of code to make the definition available - see #oxtopus's answer.
With lambda (as in #Pablo's answer), we can bind the parameter 10 to a call to superMegaIntenseFunction. All that we're doing is creating another function, that takes no arguments, and calls superMegaIntenseFunction with 10. It's just as if you'd used def to create another function like that, except that the new function doesn't get a name (because it doesn't need one).
You should be passing a string. i.e.
t = Timer('superMegaIntenseFunction(10)','from __main__ import superMegaIntenseFunction')
One way to do it would be by using partial so that the function, 'superMegaIntenseFunction' is used as a callable (ie without the ()) in the timer or directly inside timeit.timeit. Using partial will pass the argument to the function when it will be call by the timer.
from functools import partial
from timeit import timeit
print(timeit(partial(superMegaIntenseFunction, 10), number=1))
A note for future visitors. If you need to make it work in pdb debugger, and superMegaIntenseFunction is not in the global scope, you can make it work by adding to globals:
globals()['superMegaIntenseFunction'] = superMegaIntenseFunction
timeit.timeit(lambda: superMegaIntenseFunction(x))
Note that the timing overhead is a little larger in this case because of the extra function calls. [source]
I have a situation where I am attempting to port some big, complex python routines to a threaded environment.
I want to be able to, on a per-call basis, redirect the output from the function's print statement somewhere else (a logging.Logger to be specific).
I really don't want to modify the source for the code I am compiling, because I need to maintain backwards compatibility with other software that calls these modules (which is single threaded, and captures output by simply grabbing everything written to sys.stdout).
I know the best option is to do some rewriting, but I really don't have a choice here.
Edit -
Alternatively, is there any way I can override the local definition of print to point to a different function?
I could then define the local print = system print unless overwritten by a kwarg, and would only involve modify a few lines at the beginning of each routine.
In Python2.6 (and 2.7), you can use
from __future__ import print_function
Then you can change the code to use the print() function as you would for Python3
This allows you to create a module global or local function called print which will be used in preference to the builtin function
eg.
from __future__ import print_function
def f(x, print=print):
print(x*x)
f(5)
L=[]
f(6, print=L.append)
print(L)
Modifying the source code doesn't need to imply breaking backward compatibility.
What you need to do is first replace every print statement with a call to a function that does the same thing:
import sys
def _print(*args, **kw):
sep = kw.get('sep', ' ')
end = kw.get('end', '\n')
file = kw.get('file', sys.stdout)
file.write(sep.join(args))
file.write(end)
def foo():
# print "whatever","you","want"
_print("whatever","you","want")
Then the second step is to stop using the _print function directly and make it a keyword argument:
def foo(_print=_print):
...
and make sure to change all internal function calls to pass the _print function around.
Now all the existing code will continue to work and will use print, but you can pass in whatever _print function you want.
Note that the signature of _print is exactly that of the print function in more recent versions of Python, so as soon as you upgrade you can just change it to use print(). Also you may get away with using 2to3 to migrate the print statements in the existing code which should reduce the editing required.
Someone in the sixties had an idea about how to solve this but it requires a bit of alien technology. Unfortunately python has no "current environment" concept and this means you cannot provide context unless specifying it in calls as a parameter.
For handling just this specific problem what about replacing stdout with a file-like object that behaves depending on a thread-specific context ? This way the source code remains the same but for example you can get a separate log for each thread. It's even easy to do this on a specific per-call way... for example:
class MyFakeStdout:
def write(self, s):
try:
separate_logs[current_thread()].write(s)
except KeyError:
old_stdout.write(s)
and then having a function to set a logger locally to a call (with)
PS: I saw the "without touching stdout" in the title but I thought this was because you wanted only some thread to be affected. Touching it while still allowing other threads to work unaffected seems to me compatible with the question.