I asked previously how the nested functions work, but unfortunately I still don't quite get it. To understand it better, can someone please show some real-wold, practical usage examples of nested functions?
Many thanks
Your question made me curious, so I looked in some real-world code: the Python standard library. I found 67 examples of nested functions. Here are a few, with explanations.
One very simple reason to use a nested function is simply that the function you're defining doesn't need to be global, because only the enclosing function uses it. A typical example from Python's quopri.py standard library module:
def encode(input, output, quotetabs, header = 0):
...
def write(s, output=output, lineEnd='\n'):
# RFC 1521 requires that the line ending in a space or tab must have
# that trailing character encoded.
if s and s[-1:] in ' \t':
output.write(s[:-1] + quote(s[-1]) + lineEnd)
elif s == '.':
output.write(quote(s) + lineEnd)
else:
output.write(s + lineEnd)
... # 35 more lines of code that call write in several places
Here there was some common code within the encode function, so the author simply factored it out into a write function.
Another common use for nested functions is re.sub. Here's some code from the json/encode.py standard library module:
def encode_basestring(s):
"""Return a JSON representation of a Python string
"""
def replace(match):
return ESCAPE_DCT[match.group(0)]
return '"' + ESCAPE.sub(replace, s) + '"'
Here ESCAPE is a regular expression, and ESCAPE.sub(replace, s) finds all matches of ESCAPE in s and replaces each one with replace(match).
In fact, any API, like re.sub, that accepts a function as a parameter can lead to situations where nested functions are convenient. For example, in turtle.py there's some silly demo code that does this:
def baba(xdummy, ydummy):
clearscreen()
bye()
...
tri.write(" Click me!", font = ("Courier", 12, "bold") )
tri.onclick(baba, 1)
onclick expects you to pass an event-handler function, so we define one and pass it in.
Decorators are a very popular use for nested functions. Here's an example of a decorator that prints a statement before and after any call to the decorated function.
def entry_exit(f):
def new_f(*args, **kwargs):
print "Entering", f.__name__
f(*args, **kwargs)
print "Exited", f.__name__
return new_f
#entry_exit
def func1():
print "inside func1()"
#entry_exit
def func2():
print "inside func2()"
func1()
func2()
print func1.__name__
Nested functions avoid cluttering other parts of the program with other functions and variables that only make sense locally.
A function that return Fibonacci numbers could be defined as follows:
>>> def fib(n):
def rec():
return fib(n-1) + fib(n-2)
if n == 0:
return 0
elif n == 1:
return 1
else:
return rec()
>>> map(fib, range(10))
[0, 1, 1, 2, 3, 5, 8, 13, 21, 34]
EDIT: In practice, generators would be a better solution for this, but the example shows how to take advantage of nested functions.
They are useful when using functions that take other functions as input. Say you're in a function, and want to sort a list of items based on the items' value in a dict:
def f(items):
vals = {}
for i in items: vals[i] = random.randint(0,100)
def key(i): return vals[i]
items.sort(key=key)
You can just define key right there and have it use vals, a local variable.
Another use-case is callbacks.
I have only had to use nested functions when creating decorators. A nested function is basically a way of adding some behavior to a function without knowing what the function is that you are adding behavior to.
from functools import wraps
from types import InstanceType
def printCall(func):
def getArgKwargStrings(*args, **kwargs):
argsString = "".join(["%s, " % (arg) for arg in args])
kwargsString = "".join(["%s=%s, " % (key, value) for key, value in kwargs.items()])
if not len(kwargs):
if len(argsString):
argsString = argsString[:-2]
else:
kwargsString = kwargsString[:-2]
return argsString, kwargsString
#wraps(func)
def wrapper(*args, **kwargs):
ret = None
if args and isinstance(args[0], InstanceType) and getattr(args[0], func.__name__, None):
instance, args = args[0], args[1:]
argsString, kwargsString = getArgKwargStrings(*args, **kwargs)
ret = func(instance, *args, **kwargs)
print "Called %s.%s(%s%s)" % (instance.__class__.__name__, func.__name__, argsString, kwargsString)
print "Returned %s" % str(ret)
else:
argsString, kwargsString = getArgKwargStrings(*args, **kwargs)
ret = func(*args, **kwargs)
print "Called %s(%s%s)" % (func.__name__, argsString, kwargsString)
print "Returned %s" % str(ret)
return ret
return wrapper
def sayHello(name):
print "Hello, my name is %s" % (name)
if __name__ == "__main__":
sayHelloAndPrintDebug = printCall(sayHello)
name = "Nimbuz"
sayHelloAndPrintDebug(name)
Ignore all the mumbo jumbo in the "printCall" function for right now and focus only the "sayHello" function and below. What we're doing here is we want to print out how the "sayHello" function was called everytime it is called without knowing or altering what the "sayHello" function does. So we redefine the "sayHello" function by passing it to "printCall", which returns a NEW function that does what the "sayHello" function does AND prints how the "sayHello" function was called. This is the concept of decorators.
Putting "#printCall" above the sayHello definition accomplishes the same thing:
#printCall
def sayHello(name):
print "Hello, my name is %s" % (name)
if __name__ == "__main__":
name = "Nimbuz"
sayHello(name)
Yet another (very simple) example. A function that returns another function. Note how the inner function (that is returned) can use variables from the outer function's scope.
def create_adder(x):
def _adder(y):
return x + y
return _adder
add2 = create_adder(2)
add100 = create_adder(100)
>>> add2(50)
52
>>> add100(50)
150
Python Decorators
This is actually another topic to learn, but if you look at the stuff on 'Using Functions as Decorators', you'll see some examples of nested functions.
OK, besides decorators: Say you had an application where you needed to sort a list of strings based on substrings which varied from time to time. Now the sorted functions takes a key= argument which is a function of one argument: the items (strings in this case) to be sorted. So how to tell this function which substrings to sort on? A closure or nested function, is perfect for this:
def sort_key_factory(start, stop):
def sort_key(string):
return string[start: stop]
return sort_key
Simple eh? You can expand on this by encapsulating start and stop in a tuple or a slice object and then passing a sequence or iterable of these to the sort_key_factory.
Related
I often find myself using a pattern like this:
num_repeats = 123
interval = 12
for _ in xrange(num_repeats):
result = ...
if result meets condition:
break
time.sleep(interval)
else:
raise Failed despite multiple attempts
Basically, it repeats code until the correct result is returned, or the counter expires.
Although this works, it looks too verbose to me. Is it possible to "parametrize" this loop to a reusable function or context manager, like for example
with repeat(num_repeats, interval):
code
Or maybe there's something in the standard library that would do the trick?
You can use a generator which sleeps before returning repeated results.
The advantage is that your caller is still a genuine for loop, with
all the break, continue, else semantics still in tact.
def trickle_range(num_repeats, interval):
yield 0
for k in xrange(1, num_repeats):
time.sleep(interval)
yield k
for k in trickle_range(num_repeats, interval):
... do stuff, iterate or break as you like ...
You definately won't be able to use the with statement, as python only supplies hooks before and after the code has run, but not one for invoking it, ie. You can't hide a loop within a with statement.
A nice approach is to use a lambda function:
def repeat(repeats, interval, func):
for i in xrange(repeats):
if func(i):
break
time.sleep(interval)
Which you can then use quite easily:
repeat(123, 12, lambda i: condition(i))
Or something similar
One approach would be to decorate the functions you want to repeat:
def repeats_until(num_repeats, interval, condition):
def deco(f):
def func(*args, **kwargs):
for _ in xrange(num_repeats):
result = f(*args, **kwargs)
if condition(result):
return result
time.sleep(interval)
return func
return deco
And then use it like:
#repeats_until(3, 5, lambda s: s == "hello")
def take_input():
return raw_input("Say hello: ")
Example (although I can't show the wait!)
>>> take_input()
Say hello: foo
Say hello: bar
Say hello: baz
>>> take_input()
Say hello: hello
'hello'
Alternatively, to keep the condition with the called function, something like:
def repeats(num_repeats, interval):
def deco(f):
def func(*args, **kwargs):
for _ in xrange(num_repeats):
result = f(*args, **kwargs)
if result is not None: # or e.g. False if None is valid return
return result
time.sleep(interval)
return func
return deco
#repeats(3, 5)
def take_input(condition):
s = raw_input("Say hello: ")
if condition(s):
return s
ui = take_input(lambda s: s == "hello")
This relies on the decorated function returning a value (in this case the implicit None) that tells the decorator it isn't finished yet.
In some circumstances, I want to print debug-style output like this:
# module test.py
def f()
a = 5
b = 8
debug(a, b) # line 18
I want the debug function to print the following:
debug info at test.py: 18
function f
a = 5
b = 8
I am thinking it should be possible by using inspect module to locate the stack frame, then finding the appropriate line, looking up the source code in that line, getting the names of the arguments from there. The function name can be obtained by moving one stack frame up. (The values of the arguments is easy to obtain: they are passed directly to the function debug.)
Am I on the right track? Is there any recipe I can refer to?
You could do something along the following lines:
import inspect
def debug(**kwargs):
st = inspect.stack()[1]
print '%s:%d %s()' % (st[1], st[2], st[3])
for k, v in kwargs.items():
print '%s = %s' % (k, v)
def f():
a = 5
b = 8
debug(a=a, b=b) # line 12
f()
This prints out:
test.py:12 f()
a = 5
b = 8
You're generally doing it right, though it would be easier to use AOP for this kinds of tasks. Basically, instead of calling "debug" every time with every variable, you could just decorate the code with aspects which do certain things upon certain events, like upon entering the function to print passed variables and it's name.
Please refer to this site and old so post for more info.
Yeah, you are in the correct track. You may want to look at inspect.getargspec which would return a named tuple of args, varargs, keywords, defaults passed to the function.
import inspect
def f():
a = 5
b = 8
debug(a, b)
def debug(a, b):
print inspect.getargspec(debug)
f()
This is really tricky. Let me try and give a more complete answer reusing this code, and the hint about getargspec in Senthil's answer which got me triggered somehow. Btw, getargspec is deprecated in Python 3.0 and getfullarcspec should be used instead.
This works for me on a Python 3.1.2 both with explicitly calling the debug function and with using a decorator:
# from: https://stackoverflow.com/a/4493322/923794
def getfunc(func=None, uplevel=0):
"""Return tuple of information about a function
Go's up in the call stack to uplevel+1 and returns information
about the function found.
The tuple contains
name of function, function object, it's frame object,
filename and line number"""
from inspect import currentframe, getouterframes, getframeinfo
#for (level, frame) in enumerate(getouterframes(currentframe())):
# print(str(level) + ' frame: ' + str(frame))
caller = getouterframes(currentframe())[1+uplevel]
# caller is tuple of:
# frame object, filename, line number, function
# name, a list of lines of context, and index within the context
func_name = caller[3]
frame = caller[0]
from pprint import pprint
if func:
func_name = func.__name__
else:
func = frame.f_locals.get(func_name, frame.f_globals.get(func_name))
return (func_name, func, frame, caller[1], caller[2])
def debug_prt_func_args(f=None):
"""Print function name and argument with their values"""
from inspect import getargvalues, getfullargspec
(func_name, func, frame, file, line) = getfunc(func=f, uplevel=1)
argspec = getfullargspec(func)
#print(argspec)
argvals = getargvalues(frame)
print("debug info at " + file + ': ' + str(line))
print(func_name + ':' + str(argvals)) ## reformat to pretty print arg values here
return func_name
def df_dbg_prt_func_args(f):
"""Decorator: dpg_prt_func_args - Prints function name and arguments
"""
def wrapped(*args, **kwargs):
debug_prt_func_args(f)
return f(*args, **kwargs)
return wrapped
Usage:
#df_dbg_prt_func_args
def leaf_decor(*args, **kwargs):
"""Leaf level, simple function"""
print("in leaf")
def leaf_explicit(*args, **kwargs):
"""Leaf level, simple function"""
debug_prt_func_args()
print("in leaf")
def complex():
"""A complex function"""
print("start complex")
leaf_decor(3,4)
print("middle complex")
leaf_explicit(12,45)
print("end complex")
complex()
and prints:
start complex
debug info at debug.py: 54
leaf_decor:ArgInfo(args=[], varargs='args', keywords='kwargs', locals={'args': (3, 4), 'f': <function leaf_decor at 0x2aaaac048d98>, 'kwargs': {}})
in leaf
middle complex
debug info at debug.py: 67
leaf_explicit:ArgInfo(args=[], varargs='args', keywords='kwargs', locals={'args': (12, 45), 'kwargs': {}})
in leaf
end complex
The decorator cheats a bit: Since in wrapped we get the same arguments as the function itself it doesn't matter that we find and report the ArgSpec of wrapped in getfunc and debug_prt_func_args. This code could be beautified a bit, but it works alright now for the simple debug testcases I used.
Another trick you can do: If you uncomment the for-loop in getfunc you can see that inspect can give you the "context" which really is the line of source code where a function got called. This code is obviously not showing the content of any variable given to your function, but sometimes it already helps to know the variable name used one level above your called function.
As you can see, with the decorator you don't have to change the code inside the function.
Probably you'll want to pretty print the args. I've left the raw print (and also a commented out print statement) in the function so it's easier to play around with.
In writing a class recently, I initially included a __repr__ method along the following lines:
return "{}({!r}, {!r}, {!r})".format(
self.__class__.__name__,
self.arg1,
self.arg2,
self.arg3)
Repeating the '{!r}' snippet like that feels wrong and would be tedious to maintain if I ever add any more arguments to this class. However, the more robust alternatives that occurred to me aren't going to win any prizes for elegance either.
Building the format string programmatically:
fmt = "{}(%s)" % ", ".join(["{!r}"]*3)
return fmt.format(self.__class__.__name__,
self.arg1,
self.arg2,
self.arg3)
Formatting the arguments separately with str.join:
args = ", ".join(map(repr, [self.arg1, self.arg2, self.arg3]))
return "{}({})".format(self.__class__.__name__, args)
I've currently implemented the class using the last example, but I'm interested in suggestions for alternative approaches (since I'm not particularly happy with any of the above options).
Update:
Inspired by Ned's answer, I've now added the following utility function to a helper module:
def format_iter(iterable, fmt='{!r}', sep=', '):
return sep.join(fmt.format(x) for x in iterable)
>>> format_iter(range(10))
'0, 1, 2, 3, 4, 5, 6, 7, 8, 9'
>>> format_iter(range(10), sep='|')
'0|1|2|3|4|5|6|7|8|9'
>>> format_iter(range(10), fmt='{:04b}', sep='|')
'0000|0001|0010|0011|0100|0101|0110|0111|1000|1001'
>>> format_iter(range(10), fmt='{0.real}+{0.imag}j')
'0+0j, 1+0j, 2+0j, 3+0j, 4+0j, 5+0j, 6+0j, 7+0j, 8+0j, 9+0j'
Update2:
I ended up adding a second utility function, almost identical to the one in agf's answer:
def call_repr(name, *args):
return "{}({})".format(name, format_iter(args))
So the originally offending __repr__ function now looks like:
def __repr__(self):
return call_repr(self.__class__.__name__,
self.arg1,
self.arg2)
(Yes, one of the original constructor arguments went away earlier today.)
If this is a pattern you're going to repeat, I'd probably use:
# This is a static method or module-level function
def argrepr(name, *args):
return '{}({})'.format(name, ', '.join(repr(arg) for arg in args))
def __repr__(self):
return argrepr(self.__name__, self.arg1, self.arg2, self.arg3)
or
# This is a static method or module-level function
def argrepr(*args):
return '(' + ', '.join(repr(arg) for arg in args) + ')'
def __repr__(self):
return repr(self.__name__) + argrepr(self.arg1, self.arg2, self.arg3)
My inclination would be, if you don't like the code, hide it in a function:
def repr_all(*args):
return ", ".join(repr(a) for a in args)
def __repr__(self):
args = repr_all(self.arg1, self.arg2, self.arg3)
return "{}({})".format(self.__class__.__name__, args)
I might do it like:
def __repr__(self):
return "{0}({1})".format(
type(self).__name__,
", ".join(repr(arg) for arg in (self.arg1, self.arg2, self.arg3)))
or:
_init_getter = operator.attrgetter('arg1', 'arg2', 'arg3')
def __repr__(self):
return "{0}({1})".format(
type(self).__name__,
", ".join(repr(arg) for arg in self._init_getter(self)))
Suppose I have a function like f(a, b, c=None). The aim is to call the function like f(*args, **kwargs), and then construct a new set of args and kwargs such that:
If the function had default values, I should be able to acquire their values. For example, if I call it like f(1, 2), I should be able to get the tuple (1, 2, None) and/or the dictionary {'c': None}.
If the value of any of the arguments was modified inside the function, get the new value. For example, if I call it like f(1, 100000, 3) and the function does if b > 500: b = 5 modifying the local variable, I should be able to get the the tuple (1, 5, 3).
The aim here is to create a a decorator that finishes the job of a function. The original function acts as a preamble setting up the data for the actual execution, and the decorator finishes the job.
Edit: I'm adding an example of what I'm trying to do. It's a module for making proxies for other classes.
class Spam(object):
"""A fictional class that we'll make a proxy for"""
def eggs(self, start, stop, step):
"""A fictional method"""
return range(start, stop, step)
class ProxyForSpam(clsproxy.Proxy):
proxy_for = Spam
#clsproxy.signature_preamble
def eggs(self, start, stop, step=1):
start = max(0, start)
stop = min(100, stop)
And then, we'll have that:
ProxyForSpam().eggs(-10, 200) -> Spam().eggs(0, 100, 1)
ProxyForSpam().eggs(3, 4) -> Spam().eggs(3, 4, 1)
There are two recipes available here, one which requires an external library and another that uses only the standard library. They don't quite do what you want, in that they actually modify the function being executed to obtain its locals() rather than obtain the locals() after function execution, which is impossible, since the local stack no longer exists after the function finishes execution.
Another option is to see what debuggers, such as WinPDB or even the pdb module do. I suspect they use the inspect module (possibly along with others), to get the frame inside which a function is executing and retrieve locals() that way.
EDIT: After reading some code in the standard library, the file you want to look at is probably bdb.py, which should be wherever the rest of your Python standard library is. Specifically, look at set_trace() and related functions. This will give you an idea of how the Python debugger breaks into the class. You might even be able to use it directly. To get the frame to pass to set_trace() look at the inspect module.
I've stumbled upon this very need today and wanted to share my solution.
import sys
def call_function_get_frame(func, *args, **kwargs):
"""
Calls the function *func* with the specified arguments and keyword
arguments and snatches its local frame before it actually executes.
"""
frame = None
trace = sys.gettrace()
def snatch_locals(_frame, name, arg):
nonlocal frame
if frame is None and name == 'call':
frame = _frame
sys.settrace(trace)
return trace
sys.settrace(snatch_locals)
try:
result = func(*args, **kwargs)
finally:
sys.settrace(trace)
return frame, result
The idea is to use sys.trace() to catch the frame of the next 'call'. Tested on CPython 3.6.
Example usage
import types
def namespace_decorator(func):
frame, result = call_function_get_frame(func)
try:
module = types.ModuleType(func.__name__)
module.__dict__.update(frame.f_locals)
return module
finally:
del frame
#namespace_decorator
def mynamespace():
eggs = 'spam'
class Bar:
def hello(self):
print("Hello, World!")
assert mynamespace.eggs == 'spam'
mynamespace.Bar().hello()
I don't see how you could do this non-intrusively -- after the function is done executing, it doesn't exist any more -- there's no way you can reach inside something that doesn't exist.
If you can control the functions that are being used, you can do an intrusive approach like
def fn(x, y, z, vars):
'''
vars is an empty dict that we use to pass things back to the caller
'''
x += 1
y -= 1
z *= 2
vars.update(locals())
>>> updated = {}
>>> fn(1, 2, 3, updated)
>>> print updated
{'y': 1, 'x': 2, 'z': 6, 'vars': {...}}
>>>
...or you can just require that those functions return locals() -- as #Thomas K asks above, what are you really trying to do here?
Witchcraft below read on your OWN danger(!)
I have no clue what you want to do with this, it's possible but it's an awful hack...
Anyways, I HAVE WARNED YOU(!), be lucky if such things don't work in your favorite language...
from inspect import getargspec, ismethod
import inspect
def main():
#get_modified_values
def foo(a, f, b):
print a, f, b
a = 10
if a == 2:
return a
f = 'Hello World'
b = 1223
e = 1
c = 2
foo(e, 1000, b = c)
# intercept a function and retrieve the modifed values
def get_modified_values(target):
def wrapper(*args, **kwargs):
# get the applied args
kargs = getcallargs(target, *args, **kwargs)
# get the source code
src = inspect.getsource(target)
lines = src.split('\n')
# oh noes string patching of the function
unindent = len(lines[0]) - len(lines[0].lstrip())
indent = lines[0][:len(lines[0]) - len(lines[0].lstrip())]
lines[0] = ''
lines[1] = indent + 'def _temp(_args, ' + lines[1].split('(')[1]
setter = []
for k in kargs.keys():
setter.append('_args["%s"] = %s' % (k, k))
i = 0
while i < len(lines):
indent = lines[i][:len(lines[i]) - len(lines[i].lstrip())]
if lines[i].find('return ') != -1 or lines[i].find('return\n') != -1:
for e in setter:
lines.insert(i, indent + e)
i += len(setter)
elif i == len(lines) - 2:
for e in setter:
lines.insert(i + 1, indent + e)
break
i += 1
for i in range(0, len(lines)):
lines[i] = lines[i][unindent:]
data = '\n'.join(lines) + "\n"
# setup variables
frame = inspect.currentframe()
loc = inspect.getouterframes(frame)[1][0].f_locals
glob = inspect.getouterframes(frame)[1][0].f_globals
loc['_temp'] = None
# compile patched function and call it
func = compile(data, '<witchstuff>', 'exec')
eval(func, glob, loc)
loc['_temp'](kargs, *args, **kwargs)
# there you go....
print kargs
# >> {'a': 10, 'b': 1223, 'f': 'Hello World'}
return wrapper
# from python 2.7 inspect module
def getcallargs(func, *positional, **named):
"""Get the mapping of arguments to values.
A dict is returned, with keys the function argument names (including the
names of the * and ** arguments, if any), and values the respective bound
values from 'positional' and 'named'."""
args, varargs, varkw, defaults = getargspec(func)
f_name = func.__name__
arg2value = {}
# The following closures are basically because of tuple parameter unpacking.
assigned_tuple_params = []
def assign(arg, value):
if isinstance(arg, str):
arg2value[arg] = value
else:
assigned_tuple_params.append(arg)
value = iter(value)
for i, subarg in enumerate(arg):
try:
subvalue = next(value)
except StopIteration:
raise ValueError('need more than %d %s to unpack' %
(i, 'values' if i > 1 else 'value'))
assign(subarg,subvalue)
try:
next(value)
except StopIteration:
pass
else:
raise ValueError('too many values to unpack')
def is_assigned(arg):
if isinstance(arg,str):
return arg in arg2value
return arg in assigned_tuple_params
if ismethod(func) and func.im_self is not None:
# implicit 'self' (or 'cls' for classmethods) argument
positional = (func.im_self,) + positional
num_pos = len(positional)
num_total = num_pos + len(named)
num_args = len(args)
num_defaults = len(defaults) if defaults else 0
for arg, value in zip(args, positional):
assign(arg, value)
if varargs:
if num_pos > num_args:
assign(varargs, positional[-(num_pos-num_args):])
else:
assign(varargs, ())
elif 0 < num_args < num_pos:
raise TypeError('%s() takes %s %d %s (%d given)' % (
f_name, 'at most' if defaults else 'exactly', num_args,
'arguments' if num_args > 1 else 'argument', num_total))
elif num_args == 0 and num_total:
raise TypeError('%s() takes no arguments (%d given)' %
(f_name, num_total))
for arg in args:
if isinstance(arg, str) and arg in named:
if is_assigned(arg):
raise TypeError("%s() got multiple values for keyword "
"argument '%s'" % (f_name, arg))
else:
assign(arg, named.pop(arg))
if defaults: # fill in any missing values with the defaults
for arg, value in zip(args[-num_defaults:], defaults):
if not is_assigned(arg):
assign(arg, value)
if varkw:
assign(varkw, named)
elif named:
unexpected = next(iter(named))
if isinstance(unexpected, unicode):
unexpected = unexpected.encode(sys.getdefaultencoding(), 'replace')
raise TypeError("%s() got an unexpected keyword argument '%s'" %
(f_name, unexpected))
unassigned = num_args - len([arg for arg in args if is_assigned(arg)])
if unassigned:
num_required = num_args - num_defaults
raise TypeError('%s() takes %s %d %s (%d given)' % (
f_name, 'at least' if defaults else 'exactly', num_required,
'arguments' if num_required > 1 else 'argument', num_total))
return arg2value
main()
Output:
1 1000 2
{'a': 10, 'b': 1223, 'f': 'Hello World'}
There you go... I'm not responsible for any small children that get eaten by demons or something the like (or if it breaks on complicated functions).
PS: The inspect module is the pure EVIL.
Since you are trying to manipulate variables in one function, and do some job based on those variables on another function, the cleanest way to do it is having these variables to be an object's attributes.
It could be a dictionary - that could be defined inside the decorator - therefore access to it inside the decorated function would be as a "nonlocal" variable. That cleans up the default parameter tuple of this dictionary, that #bgporter proposed.:
def eggs(self, a, b, c=None):
# nonlocal parms ## uncomment in Python 3
parms["a"] = a
...
To be even more clean, you probably should have all these parameters as attributes of the instance (self) - so that no "magical" variable has to be used inside the decorated function.
As for doing it "magically" without having the parameters set as attributes of certain object explicitly, nor having the decorated function to return the parameters themselves (which is also an option) - that is, to have it to work transparently with any decorated function - I can't think of a way that does not involve manipulating the bytecode of the function itself.
If you can think of a way to make the wrapped function raise an exception at return time, you could trap the exception and check the execution trace.
If it is so important to do it automatically that you consider altering the function bytecode an option, feel free to ask me further.
I feel like I should know this, but I haven't been able to figure it out...
I want to get the name of a method--which happens to be an integration test--from inside it so it can print out some diagnostic text. I can, of course, just hard-code the method's name in the string, but I'd like to make the test a little more DRY if possible.
This seems to be the simplest way using module inspect:
import inspect
def somefunc(a,b,c):
print "My name is: %s" % inspect.stack()[0][3]
You could generalise this with:
def funcname():
return inspect.stack()[1][3]
def somefunc(a,b,c):
print "My name is: %s" % funcname()
Credit to Stefaan Lippens which was found via google.
The answers involving introspection via inspect and the like are reasonable. But there may be another option, depending on your situation:
If your integration test is written with the unittest module, then you could use self.id() within your TestCase.
This decorator makes the name of the method available inside the function by passing it as a keyword argument.
from functools import wraps
def pass_func_name(func):
"Name of decorated function will be passed as keyword arg _func_name"
#wraps(func)
def _pass_name(*args, **kwds):
kwds['_func_name'] = func.func_name
return func(*args, **kwds)
return _pass_name
You would use it this way:
#pass_func_name
def sum(a, b, _func_name):
print "running function %s" % _func_name
return a + b
print sum(2, 4)
But maybe you'd want to write what you want directly inside the decorator itself. Then the code is an example of a way to get the function name in a decorator. If you give more details about what you want to do in the function, that requires the name, maybe I can suggest something else.
# file "foo.py"
import sys
import os
def LINE( back = 0 ):
return sys._getframe( back + 1 ).f_lineno
def FILE( back = 0 ):
return sys._getframe( back + 1 ).f_code.co_filename
def FUNC( back = 0):
return sys._getframe( back + 1 ).f_code.co_name
def WHERE( back = 0 ):
frame = sys._getframe( back + 1 )
return "%s/%s %s()" % ( os.path.basename( frame.f_code.co_filename ),
frame.f_lineno, frame.f_code.co_name )
def testit():
print "Here in %s, file %s, line %s" % ( FUNC(), FILE(), LINE() )
print "WHERE says '%s'" % WHERE()
testit()
Output:
$ python foo.py
Here in testit, file foo.py, line 17
WHERE says 'foo.py/18 testit()'
Use "back = 1" to find info regarding two levels back down the stack, etc.
I think the traceback module might have what you're looking for. In particular, the extract_stack function looks like it will do the job.
To elaborate on #mhawke's answer:
Rather than
def funcname():
return inspect.stack()[1][3]
You can use
def funcname():
frame = inspect.currentframe().f_back
return inspect.getframeinfo(frame).function
Which, on my machine, is about 5x faster than the original version according to timeit.