I can go one better than @jme. Here's a version of his decorator that indents and dedents according to your location in the call stack:
import functools
# a factory for decorators
def create_tracer(tab_width):
indentation_level = 0
def decorator(f): # a decorator is a function which takes a function and returns a function
@functools.wraps(f)
def wrapper(*args): # we wish to extend the function that was passed to the decorator, so we define a wrapper function to return
nonlocal indentation_level # python 3 only, sorry
msg = " " * indentation_level + "{}({})".format(f.__name__, ", ".join([str(a) for a in args]))
print(msg)
indentation_level += tab_width # mutate the closure so the next function that is called gets a deeper indentation level
result = f(*args)
indentation_level -= tab_width
return result
return wrapper
return decorator
tracer = create_tracer(4) # create the decorator itself
@tracer
def f1():
x = f2(5)
return f3(x)
@tracer
def f2(x):
return f3(2)*x
@tracer
def f3(x):
return 4*x
f1()
Output:
f1()
f2(5)
f3(2)
f3(40)
The nonlocal statement allows us to mutate the indentation_level in the outer scope. Upon entering a function, we increase the indentation level so that the next print gets indented further. Then upon exiting we decrease it again.
This is called decorator syntax. It's purely 'syntactic sugar'; the transformation into equivalent code without @ is very simple.
@d
def f():
pass
is just the same as:
def f():
pass
f = d(f)
As you can see, @ simply uses the decorator to process the decorated function in some way, and replaces the original function with the result, just like in @jme's answer. It's like Invasion of the Body Snatchers; we are replacing f with something that looks similar to f but behaves differently.
If you're stuck on Python 2, you can simulate the nonlocal statement by using a class with an instance variable. This might make a bit more sense to you, if you've never used decorators before.
# a class which acts like a decorator
class Tracer(object):
def __init__(self, tab_width):
self.tab_width = tab_width
self.indentation_level = 0
# make the class act like a function (which takes a function and returns a function)
def __call__(self, f):
@functools.wraps(f)
def wrapper(*args):
msg = " " * self.indentation_level + "{}({})".format(f.__name__, ", ".join([str(a) for a in args]))
print msg
self.indentation_level += self.tab_width
result = f(*args)
self.indentation_level -= self.tab_width
return result
return wrapper
tracer = Tracer(4)
@tracer
def f1():
# etc, as above
You mentioned that you're not allowed to change the existing functions. You can retro-fit the decorator by messing around with globals() (though this generally isn't a good idea unless you really need to do it):
for name, val in globals().items(): # use iteritems() in Python 2
if name.contains('f'): # look for the functions we wish to trace
wrapped_func = tracer(val)
globals()[name] = wrapped_func # overwrite the function with our wrapped version
If you don't have access to the source of the module in question, you can achieve something very similar by inspecting the imported module and mutating the items it exports.
The sky's the limit with this approach. You could build this into an industrial-strength code analysis tool by storing the calls in some sort of graph data structure, instead of simply indenting and printing. You could then query your data to answer questions like "which functions in this module are called the most?" or "which functions are the slowest?". In fact, that's a great idea for a library...
ast,compiler, andinspectmodules, usinginspect.getsource(...)to retrieve the source code of the Python file itself, and then usingcompiler.parse(...)to get an AST data structure, which you can examine to figure out what's calling what.sys.settraceand inspecting the frame objects should be easier and far more accurate than any static analysis (which is what Jeremy Banks's suggestion implies). On the other hand, it'll require copious amounts of CPython-specific black magic.