In Python, is there a way for an instance of an object to see the variable name it's assigned to? Take the following for example:
class MyObject(object):
pass
x = MyObject()
Is it possible for MyObject to see it's been assigned to a variable name x at any point? Like in it's __init__ method?
Yes, it is possible*. However, the problem is more difficult than it seems upon first glance:
There may be multiple names assigned to the same object.
There may be no names at all.
The same name(s) may refer to some other object(s) in a different namespace.
Regardless, knowing how to find the names of an object can sometimes be useful for debugging purposes - and here is how to do it:
import gc, inspect
def find_names(obj):
frame = inspect.currentframe()
for frame in iter(lambda: frame.f_back, None):
frame.f_locals
obj_names = []
for referrer in gc.get_referrers(obj):
if isinstance(referrer, dict):
for k, v in referrer.items():
if v is obj:
obj_names.append(k)
return obj_names
If you're ever tempted to base logic around the names of your variables, pause for a moment and consider if redesign/refactor of code could solve the problem. The need to recover an object's name from the object itself usually means that underlying data structures in your program need a rethink.
* at least in Cpython
As many others have said, it can't be done properly. However inspired by jsbueno's, I have an alternative to his solution.
Like his solution, I inspect the callers stack frame, which means it only works properly for Python-implemented callers (see note below). Unlike him, I inspect the bytecode of the caller directly (instead of loading and parsing the source code). Using Python 3.4+'s dis.get_instructions() this can be done with some hope of minimal compatibility. Though this is still some hacky code.
import inspect
import dis
def take1(iterator):
try:
return next(iterator)
except StopIteration:
raise Exception("missing bytecode instruction") from None
def take(iterator, count):
for x in range(count):
yield take1(iterator)
def get_assigned_name(frame):
"""Takes a frame and returns a description of the name(s) to which the
currently executing CALL_FUNCTION instruction's value will be assigned.
fn() => None
a = fn() => "a"
a, b = fn() => ("a", "b")
a.a2.a3, b, c* = fn() => ("a.a2.a3", "b", Ellipsis)
"""
iterator = iter(dis.get_instructions(frame.f_code))
for instr in iterator:
if instr.offset == frame.f_lasti:
break
else:
assert False, "bytecode instruction missing"
assert instr.opname.startswith('CALL_')
instr = take1(iterator)
if instr.opname == 'POP_TOP':
raise ValueError("not assigned to variable")
return instr_dispatch(instr, iterator)
def instr_dispatch(instr, iterator):
opname = instr.opname
if (opname == 'STORE_FAST' # (co_varnames)
or opname == 'STORE_GLOBAL' # (co_names)
or opname == 'STORE_NAME' # (co_names)
or opname == 'STORE_DEREF'): # (co_cellvars++co_freevars)
return instr.argval
if opname == 'UNPACK_SEQUENCE':
return tuple(instr_dispatch(instr, iterator)
for instr in take(iterator, instr.arg))
if opname == 'UNPACK_EX':
return (*tuple(instr_dispatch(instr, iterator)
for instr in take(iterator, instr.arg)),
Ellipsis)
# Note: 'STORE_SUBSCR' and 'STORE_ATTR' should not be possible here.
# `lhs = rhs` in Python will evaluate `lhs` after `rhs`.
# Thus `x.attr = rhs` will first evalute `rhs` then load `a` and finally
# `STORE_ATTR` with `attr` as instruction argument. `a` can be any
# complex expression, so full support for understanding what a
# `STORE_ATTR` will target requires decoding the full range of expression-
# related bytecode instructions. Even figuring out which `STORE_ATTR`
# will use our return value requires non-trivial understanding of all
# expression-related bytecode instructions.
# Thus we limit ourselfs to loading a simply variable (of any kind)
# and a arbitary number of LOAD_ATTR calls before the final STORE_ATTR.
# We will represents simply a string like `my_var.loaded.loaded.assigned`
if opname in {'LOAD_CONST', 'LOAD_DEREF', 'LOAD_FAST',
'LOAD_GLOBAL', 'LOAD_NAME'}:
return instr.argval + "." + ".".join(
instr_dispatch_for_load(instr, iterator))
raise NotImplementedError("assignment could not be parsed: "
"instruction {} not understood"
.format(instr))
def instr_dispatch_for_load(instr, iterator):
instr = take1(iterator)
opname = instr.opname
if opname == 'LOAD_ATTR':
yield instr.argval
yield from instr_dispatch_for_load(instr, iterator)
elif opname == 'STORE_ATTR':
yield instr.argval
else:
raise NotImplementedError("assignment could not be parsed: "
"instruction {} not understood"
.format(instr))
Note: C-implemented functions don't show up as Python stack frames and are thus hidden to this script. This will result in false positives. Consider Python function f() which calls a = g(). g() is C-implemented and calls b = f2(). When f2() tries to lookup up the assigned name, it will get a instead of b because the script is oblivious to C functions. (At least this is how I guess it will work :P )
Usage example:
class MyItem():
def __init__(self):
self.name = get_assigned_name(inspect.currentframe().f_back)
abc = MyItem()
assert abc.name == "abc"
No. Objects and names live in separate dimensions. One object can have many names during its lifetime, and it's impossible to determine which one might be the one you want. Even in here:
class Foo(object):
def __init__(self): pass
x = Foo()
two names denote the same object (self when __init__ runs, x in global scope).
Here is a simple function to achieve what you want, assuming you wish to retrieve the name of the variable where the instance is assigned from a method call :
import inspect
def get_instance_var_name(method_frame, instance):
parent_frame = method_frame.f_back
matches = {k: v for k,v in parent_frame.f_globals.items() if v is instance}
assert len(matches) < 2
return list(matches.keys())[0] if matches else None
Here is an usage example :
class Bar:
def foo(self):
print(get_instance_var_name(inspect.currentframe(), self))
bar = Bar()
bar.foo() # prints 'bar'
def nested():
bar.foo()
nested() # prints 'bar'
Bar().foo() # prints None
It can't be ordinarily done, though this can be achieved by using introspection and facilities meant for debugging a program. The code must run from a ".py" file though, and not from just compiled bytecode, or inside a zipped module - as it relies on the reading of the file source code, from within the method that should find about "where it is running".
The trick is to access the execution frame where the object was initialized from - with inspect.currentframe - the frame object has a "f_lineno" value which states the line number where the call to the object method (in this case, __init__) has been called. The function inspect.filename allows one to retrieve the source code for the file, and fetch the apropriate line number.
A naive parse then peek the part preeceding an "=" sign, and assumes it is the variable that will contain the object.
from inspect import currentframe, getfile
class A(object):
def __init__(self):
f = currentframe(1)
filename = getfile(f)
code_line = open(filename).readlines()[f.f_lineno - 1]
assigned_variable = code_line.split("=")[0].strip()
print assigned_variable
my_name = A()
other_name = A()
That won't work for multiple assignents, expressions composing with the object before the assignemtn is made, objects being appended to lists or added to dictionaries or sets, object instantiation in intialization of for loops, and God knows which more situations --
And have in mind that after the first attribution, the object could be referenced by any other variable as well.
Botton line: it is possible, but as a toy - it can't be used i production code -
just have the varibal name to be passed as a string during object initialization, just as one has to do when creating a collections.namedtuple
The "right way" to do it, if you are needing the name, is to explicitly pass the name to the object initialization, as a string parameter, like in:
class A(object):
def __init__(self, name):
self.name = name
x = A("x")
And still, if absolutely need to type the objects'name only once, there is another way - read on.
Due to Python's syntax, some special assignments, not using the "=" operator do allow an object to know it is assigned name. So, other statemtns that perform assignents in Python are the for, with, def and class keywords - It is possible to abuse this, as specfically a class creation and a function definition are assignment statements that create objects which "know" their names.
Let's focus on the def statement. It ordinarily creates a function. But using a decorator you can use "def" to create any kind of object - and have the name used for the function available to the constructor:
class MyObject(object):
def __new__(cls, func):
# Calls the superclass constructor and actually instantiates the object:
self = object.__new__(cls)
#retrieve the function name:
self.name = func.func_name
#returns an instance of this class, instead of a decorated function:
return self
def __init__(self, func):
print "My name is ", self.name
#and the catch is that you can't use "=" to create this object, you have to do:
#MyObject
def my_name(): pass
(This last way of doing it could be used in production code, unlike the one which resorts to reading the source file)
assuming this:
class MyObject(object):
pass
x = MyObject()
then you can search through the environment by the object's id, returning the key when there is a match.
keys = list(globals().keys()) # list all variable names
target = id(x) # find the id of your object
for k in keys:
value_memory_address = id(globals()[k]) # fetch id of every object
if value_memory_address == target:
print(globals()[k], k) # if there is a variable assigned to that id, then it is a variable that points to your object
I was independently working on this and have the following. It's not as comprehensive as driax's answer, but efficiently covers the case described and doesn't rely on searching for the object's id in global variables or parsing source code...
import sys
import dis
class MyObject:
def __init__(self):
# uses bytecode magic to find the name of the assigned variable
f = sys._getframe(1) # get stack frame of caller (depth=1)
# next op should be STORE_NAME (current op calls the constructor)
opname = dis.opname[f.f_code.co_code[f.f_lasti+2]]
if opname == 'STORE_NAME': # not all objects will be assigned a name
# STORE_NAME argument is the name index
namei = f.f_code.co_code[f.f_lasti+3]
self.name = f.f_code.co_names[namei]
else:
self.name = None
x = MyObject()
x.name == 'x'
I know that Python does not support method overloading, but I've run into a problem that I can't seem to solve in a nice Pythonic way.
I am making a game where a character needs to shoot a variety of bullets, but how do I write different functions for creating these bullets? For example suppose I have a function that creates a bullet travelling from point A to B with a given speed. I would write a function like this:
def add_bullet(sprite, start, headto, speed):
# Code ...
But I want to write other functions for creating bullets like:
def add_bullet(sprite, start, direction, speed):
def add_bullet(sprite, start, headto, spead, acceleration):
def add_bullet(sprite, script): # For bullets that are controlled by a script
def add_bullet(sprite, curve, speed): # for bullets with curved paths
# And so on ...
And so on with many variations. Is there a better way to do it without using so many keyword arguments cause its getting kinda ugly fast. Renaming each function is pretty bad too because you get either add_bullet1, add_bullet2, or add_bullet_with_really_long_name.
To address some answers:
No I can't create a Bullet class hierarchy because thats too slow. The actual code for managing bullets is in C and my functions are wrappers around C API.
I know about the keyword arguments but checking for all sorts of combinations of parameters is getting annoying, but default arguments help allot like acceleration=0
What you are asking for is called multiple dispatch. See Julia language examples which demonstrates different types of dispatches.
However, before looking at that, we'll first tackle why overloading is not really what you want in Python.
Why Not Overloading?
First, one needs to understand the concept of overloading and why it's not applicable to Python.
When working with languages that can discriminate data types at
compile-time, selecting among the alternatives can occur at
compile-time. The act of creating such alternative functions for
compile-time selection is usually referred to as overloading a
function. (Wikipedia)
Python is a dynamically typed language, so the concept of overloading simply does not apply to it. However, all is not lost, since we can create such alternative functions at run-time:
In programming languages that defer data type identification until
run-time the selection among alternative
functions must occur at run-time, based on the dynamically determined
types of function arguments. Functions whose alternative
implementations are selected in this manner are referred to most
generally as multimethods. (Wikipedia)
So we should be able to do multimethods in Python—or, as it is alternatively called: multiple dispatch.
Multiple dispatch
The multimethods are also called multiple dispatch:
Multiple dispatch or multimethods is the feature of some
object-oriented programming languages in which a function or method
can be dynamically dispatched based on the run time (dynamic) type of
more than one of its arguments. (Wikipedia)
Python does not support this out of the box1, but, as it happens, there is an excellent Python package called multipledispatch that does exactly that.
Solution
Here is how we might use multipledispatch2 package to implement your methods:
>>> from multipledispatch import dispatch
>>> from collections import namedtuple
>>> from types import * # we can test for lambda type, e.g.:
>>> type(lambda a: 1) == LambdaType
True
>>> Sprite = namedtuple('Sprite', ['name'])
>>> Point = namedtuple('Point', ['x', 'y'])
>>> Curve = namedtuple('Curve', ['x', 'y', 'z'])
>>> Vector = namedtuple('Vector', ['x','y','z'])
>>> #dispatch(Sprite, Point, Vector, int)
... def add_bullet(sprite, start, direction, speed):
... print("Called Version 1")
...
>>> #dispatch(Sprite, Point, Point, int, float)
... def add_bullet(sprite, start, headto, speed, acceleration):
... print("Called version 2")
...
>>> #dispatch(Sprite, LambdaType)
... def add_bullet(sprite, script):
... print("Called version 3")
...
>>> #dispatch(Sprite, Curve, int)
... def add_bullet(sprite, curve, speed):
... print("Called version 4")
...
>>> sprite = Sprite('Turtle')
>>> start = Point(1,2)
>>> direction = Vector(1,1,1)
>>> speed = 100 #km/h
>>> acceleration = 5.0 #m/s**2
>>> script = lambda sprite: sprite.x * 2
>>> curve = Curve(3, 1, 4)
>>> headto = Point(100, 100) # somewhere far away
>>> add_bullet(sprite, start, direction, speed)
Called Version 1
>>> add_bullet(sprite, start, headto, speed, acceleration)
Called version 2
>>> add_bullet(sprite, script)
Called version 3
>>> add_bullet(sprite, curve, speed)
Called version 4
1. Python 3 currently supports single dispatch
2. Take care not to use multipledispatch in a multi-threaded environment or you will get weird behavior.
Python does support "method overloading" as you present it. In fact, what you just describe is trivial to implement in Python, in so many different ways, but I would go with:
class Character(object):
# your character __init__ and other methods go here
def add_bullet(self, sprite=default, start=default,
direction=default, speed=default, accel=default,
curve=default):
# do stuff with your arguments
In the above code, default is a plausible default value for those arguments, or None. You can then call the method with only the arguments you are interested in, and Python will use the default values.
You could also do something like this:
class Character(object):
# your character __init__ and other methods go here
def add_bullet(self, **kwargs):
# here you can unpack kwargs as (key, values) and
# do stuff with them, and use some global dictionary
# to provide default values and ensure that ``key``
# is a valid argument...
# do stuff with your arguments
Another alternative is to directly hook the desired function directly to the class or instance:
def some_implementation(self, arg1, arg2, arg3):
# implementation
my_class.add_bullet = some_implementation_of_add_bullet
Yet another way is to use an abstract factory pattern:
class Character(object):
def __init__(self, bfactory, *args, **kwargs):
self.bfactory = bfactory
def add_bullet(self):
sprite = self.bfactory.sprite()
speed = self.bfactory.speed()
# do stuff with your sprite and speed
class pretty_and_fast_factory(object):
def sprite(self):
return pretty_sprite
def speed(self):
return 10000000000.0
my_character = Character(pretty_and_fast_factory(), a1, a2, kw1=v1, kw2=v2)
my_character.add_bullet() # uses pretty_and_fast_factory
# now, if you have another factory called "ugly_and_slow_factory"
# you can change it at runtime in python by issuing
my_character.bfactory = ugly_and_slow_factory()
# In the last example you can see abstract factory and "method
# overloading" (as you call it) in action
You can use "roll-your-own" solution for function overloading. This one is copied from Guido van Rossum's article about multimethods (because there is little difference between multimethods and overloading in Python):
registry = {}
class MultiMethod(object):
def __init__(self, name):
self.name = name
self.typemap = {}
def __call__(self, *args):
types = tuple(arg.__class__ for arg in args) # a generator expression!
function = self.typemap.get(types)
if function is None:
raise TypeError("no match")
return function(*args)
def register(self, types, function):
if types in self.typemap:
raise TypeError("duplicate registration")
self.typemap[types] = function
def multimethod(*types):
def register(function):
name = function.__name__
mm = registry.get(name)
if mm is None:
mm = registry[name] = MultiMethod(name)
mm.register(types, function)
return mm
return register
The usage would be
from multimethods import multimethod
import unittest
# 'overload' makes more sense in this case
overload = multimethod
class Sprite(object):
pass
class Point(object):
pass
class Curve(object):
pass
#overload(Sprite, Point, Direction, int)
def add_bullet(sprite, start, direction, speed):
# ...
#overload(Sprite, Point, Point, int, int)
def add_bullet(sprite, start, headto, speed, acceleration):
# ...
#overload(Sprite, str)
def add_bullet(sprite, script):
# ...
#overload(Sprite, Curve, speed)
def add_bullet(sprite, curve, speed):
# ...
Most restrictive limitations at the moment are:
methods are not supported, only functions that are not class members;
inheritance is not handled;
kwargs are not supported;
registering new functions should be done at import time thing is not thread-safe
A possible option is to use the multipledispatch module as detailed here:
http://matthewrocklin.com/blog/work/2014/02/25/Multiple-Dispatch
Instead of doing this:
def add(self, other):
if isinstance(other, Foo):
...
elif isinstance(other, Bar):
...
else:
raise NotImplementedError()
You can do this:
from multipledispatch import dispatch
#dispatch(int, int)
def add(x, y):
return x + y
#dispatch(object, object)
def add(x, y):
return "%s + %s" % (x, y)
With the resulting usage:
>>> add(1, 2)
3
>>> add(1, 'hello')
'1 + hello'
In Python 3.4 PEP-0443. Single-dispatch generic functions was added.
Here is a short API description from PEP.
To define a generic function, decorate it with the #singledispatch decorator. Note that the dispatch happens on the type of the first argument. Create your function accordingly:
from functools import singledispatch
#singledispatch
def fun(arg, verbose=False):
if verbose:
print("Let me just say,", end=" ")
print(arg)
To add overloaded implementations to the function, use the register() attribute of the generic function. This is a decorator, taking a type parameter and decorating a function implementing the operation for that type:
#fun.register(int)
def _(arg, verbose=False):
if verbose:
print("Strength in numbers, eh?", end=" ")
print(arg)
#fun.register(list)
def _(arg, verbose=False):
if verbose:
print("Enumerate this:")
for i, elem in enumerate(arg):
print(i, elem)
The #overload decorator was added with type hints (PEP 484).
While this doesn't change the behaviour of Python, it does make it easier to understand what is going on, and for mypy to detect errors.
See: Type hints and PEP 484
This type of behaviour is typically solved (in OOP languages) using polymorphism. Each type of bullet would be responsible for knowing how it travels. For instance:
class Bullet(object):
def __init__(self):
self.curve = None
self.speed = None
self.acceleration = None
self.sprite_image = None
class RegularBullet(Bullet):
def __init__(self):
super(RegularBullet, self).__init__()
self.speed = 10
class Grenade(Bullet):
def __init__(self):
super(Grenade, self).__init__()
self.speed = 4
self.curve = 3.5
add_bullet(Grendade())
def add_bullet(bullet):
c_function(bullet.speed, bullet.curve, bullet.acceleration, bullet.sprite, bullet.x, bullet.y)
void c_function(double speed, double curve, double accel, char[] sprite, ...) {
if (speed != null && ...) regular_bullet(...)
else if (...) curved_bullet(...)
//..etc..
}
Pass as many arguments to the c_function that exist, and then do the job of determining which c function to call based on the values in the initial c function. So, Python should only ever be calling the one c function. That one c function looks at the arguments, and then can delegate to other c functions appropriately.
You're essentially just using each subclass as a different data container, but by defining all the potential arguments on the base class, the subclasses are free to ignore the ones they do nothing with.
When a new type of bullet comes along, you can simply define one more property on the base, change the one python function so that it passes the extra property, and the one c_function that examines the arguments and delegates appropriately. It doesn't sound too bad I guess.
It is impossible by definition to overload a function in python (read on for details), but you can achieve something similar with a simple decorator
class overload:
def __init__(self, f):
self.cases = {}
def args(self, *args):
def store_function(f):
self.cases[tuple(args)] = f
return self
return store_function
def __call__(self, *args):
function = self.cases[tuple(type(arg) for arg in args)]
return function(*args)
You can use it like this
#overload
def f():
pass
#f.args(int, int)
def f(x, y):
print('two integers')
#f.args(float)
def f(x):
print('one float')
f(5.5)
f(1, 2)
Modify it to adapt it to your use case.
A clarification of concepts
function dispatch: there are multiple functions with the same name. Which one should be called? two strategies
static/compile-time dispatch (aka. "overloading"). decide which function to call based on the compile-time type of the arguments. In all dynamic languages, there is no compile-time type, so overloading is impossible by definition
dynamic/run-time dispatch: decide which function to call based on the runtime type of the arguments. This is what all OOP languages do: multiple classes have the same methods, and the language decides which one to call based on the type of self/this argument. However, most languages only do it for the this argument only. The above decorator extends the idea to multiple parameters.
To clear up, assume that we define, in a hypothetical static language, the functions
void f(Integer x):
print('integer called')
void f(Float x):
print('float called')
void f(Number x):
print('number called')
Number x = new Integer('5')
f(x)
x = new Number('3.14')
f(x)
With static dispatch (overloading) you will see "number called" twice, because x has been declared as Number, and that's all overloading cares about. With dynamic dispatch you will see "integer called, float called", because those are the actual types of x at the time the function is called.
By passing keyword args.
def add_bullet(**kwargs):
#check for the arguments listed above and do the proper things
Python 3.8 added functools.singledispatchmethod
Transform a method into a single-dispatch generic function.
To define a generic method, decorate it with the #singledispatchmethod
decorator. Note that the dispatch happens on the type of the first
non-self or non-cls argument, create your function accordingly:
from functools import singledispatchmethod
class Negator:
#singledispatchmethod
def neg(self, arg):
raise NotImplementedError("Cannot negate a")
#neg.register
def _(self, arg: int):
return -arg
#neg.register
def _(self, arg: bool):
return not arg
negator = Negator()
for v in [42, True, "Overloading"]:
neg = negator.neg(v)
print(f"{v=}, {neg=}")
Output
v=42, neg=-42
v=True, neg=False
NotImplementedError: Cannot negate a
#singledispatchmethod supports nesting with other decorators such as
#classmethod. Note that to allow for dispatcher.register,
singledispatchmethod must be the outer most decorator. Here is the
Negator class with the neg methods being class bound:
from functools import singledispatchmethod
class Negator:
#singledispatchmethod
#staticmethod
def neg(arg):
raise NotImplementedError("Cannot negate a")
#neg.register
def _(arg: int) -> int:
return -arg
#neg.register
def _(arg: bool) -> bool:
return not arg
for v in [42, True, "Overloading"]:
neg = Negator.neg(v)
print(f"{v=}, {neg=}")
Output:
v=42, neg=-42
v=True, neg=False
NotImplementedError: Cannot negate a
The same pattern can be used for other similar decorators:
staticmethod, abstractmethod, and others.
I think your basic requirement is to have a C/C++-like syntax in Python with the least headache possible. Although I liked Alexander Poluektov's answer it doesn't work for classes.
The following should work for classes. It works by distinguishing by the number of non-keyword arguments (but it doesn't support distinguishing by type):
class TestOverloading(object):
def overloaded_function(self, *args, **kwargs):
# Call the function that has the same number of non-keyword arguments.
getattr(self, "_overloaded_function_impl_" + str(len(args)))(*args, **kwargs)
def _overloaded_function_impl_3(self, sprite, start, direction, **kwargs):
print "This is overload 3"
print "Sprite: %s" % str(sprite)
print "Start: %s" % str(start)
print "Direction: %s" % str(direction)
def _overloaded_function_impl_2(self, sprite, script):
print "This is overload 2"
print "Sprite: %s" % str(sprite)
print "Script: "
print script
And it can be used simply like this:
test = TestOverloading()
test.overloaded_function("I'm a Sprite", 0, "Right")
print
test.overloaded_function("I'm another Sprite", "while x == True: print 'hi'")
Output:
This is overload 3
Sprite: I'm a Sprite
Start: 0
Direction: Right
This is overload 2
Sprite: I'm another Sprite
Script:
while x == True: print 'hi'
You can achieve this with the following Python code:
#overload
def test(message: str):
return message
#overload
def test(number: int):
return number + 1
Either use multiple keyword arguments in the definition, or create a Bullet hierarchy whose instances are passed to the function.
I think a Bullet class hierarchy with the associated polymorphism is the way to go. You can effectively overload the base class constructor by using a metaclass so that calling the base class results in the creation of the appropriate subclass object. Below is some sample code to illustrate the essence of what I mean.
Updated
The code has been modified to run under both Python 2 and 3 to keep it relevant. This was done in a way that avoids the use Python's explicit metaclass syntax, which varies between the two versions.
To accomplish that objective, a BulletMetaBase instance of the BulletMeta class is created by explicitly calling the metaclass when creating the Bullet baseclass (rather than using the __metaclass__= class attribute or via a metaclass keyword argument depending on the Python version).
class BulletMeta(type):
def __new__(cls, classname, bases, classdict):
""" Create Bullet class or a subclass of it. """
classobj = type.__new__(cls, classname, bases, classdict)
if classname != 'BulletMetaBase':
if classname == 'Bullet': # Base class definition?
classobj.registry = {} # Initialize subclass registry.
else:
try:
alias = classdict['alias']
except KeyError:
raise TypeError("Bullet subclass %s has no 'alias'" %
classname)
if alias in Bullet.registry: # unique?
raise TypeError("Bullet subclass %s's alias attribute "
"%r already in use" % (classname, alias))
# Register subclass under the specified alias.
classobj.registry[alias] = classobj
return classobj
def __call__(cls, alias, *args, **kwargs):
""" Bullet subclasses instance factory.
Subclasses should only be instantiated by calls to the base
class with their subclass' alias as the first arg.
"""
if cls != Bullet:
raise TypeError("Bullet subclass %r objects should not to "
"be explicitly constructed." % cls.__name__)
elif alias not in cls.registry: # Bullet subclass?
raise NotImplementedError("Unknown Bullet subclass %r" %
str(alias))
# Create designated subclass object (call its __init__ method).
subclass = cls.registry[alias]
return type.__call__(subclass, *args, **kwargs)
class Bullet(BulletMeta('BulletMetaBase', (object,), {})):
# Presumably you'd define some abstract methods that all here
# that would be supported by all subclasses.
# These definitions could just raise NotImplementedError() or
# implement the functionality is some sub-optimal generic way.
# For example:
def fire(self, *args, **kwargs):
raise NotImplementedError(self.__class__.__name__ + ".fire() method")
# Abstract base class's __init__ should never be called.
# If subclasses need to call super class's __init__() for some
# reason then it would need to be implemented.
def __init__(self, *args, **kwargs):
raise NotImplementedError("Bullet is an abstract base class")
# Subclass definitions.
class Bullet1(Bullet):
alias = 'B1'
def __init__(self, sprite, start, direction, speed):
print('creating %s object' % self.__class__.__name__)
def fire(self, trajectory):
print('Bullet1 object fired with %s trajectory' % trajectory)
class Bullet2(Bullet):
alias = 'B2'
def __init__(self, sprite, start, headto, spead, acceleration):
print('creating %s object' % self.__class__.__name__)
class Bullet3(Bullet):
alias = 'B3'
def __init__(self, sprite, script): # script controlled bullets
print('creating %s object' % self.__class__.__name__)
class Bullet4(Bullet):
alias = 'B4'
def __init__(self, sprite, curve, speed): # for bullets with curved paths
print('creating %s object' % self.__class__.__name__)
class Sprite: pass
class Curve: pass
b1 = Bullet('B1', Sprite(), (10,20,30), 90, 600)
b2 = Bullet('B2', Sprite(), (-30,17,94), (1,-1,-1), 600, 10)
b3 = Bullet('B3', Sprite(), 'bullet42.script')
b4 = Bullet('B4', Sprite(), Curve(), 720)
b1.fire('uniform gravity')
b2.fire('uniform gravity')
Output:
creating Bullet1 object
creating Bullet2 object
creating Bullet3 object
creating Bullet4 object
Bullet1 object fired with uniform gravity trajectory
Traceback (most recent call last):
File "python-function-overloading.py", line 93, in <module>
b2.fire('uniform gravity') # NotImplementedError: Bullet2.fire() method
File "python-function-overloading.py", line 49, in fire
raise NotImplementedError(self.__class__.__name__ + ".fire() method")
NotImplementedError: Bullet2.fire() method
You can easily implement function overloading in Python. Here is an example using floats and integers:
class OverloadedFunction:
def __init__(self):
self.router = {int : self.f_int ,
float: self.f_float}
def __call__(self, x):
return self.router[type(x)](x)
def f_int(self, x):
print('Integer Function')
return x**2
def f_float(self, x):
print('Float Function (Overloaded)')
return x**3
# f is our overloaded function
f = OverloadedFunction()
print(f(3 ))
print(f(3.))
# Output:
# Integer Function
# 9
# Float Function (Overloaded)
# 27.0
The main idea behind the code is that a class holds the different (overloaded) functions that you would like to implement, and a Dictionary works as a router, directing your code towards the right function depending on the input type(x).
PS1. In case of custom classes, like Bullet1, you can initialize the internal dictionary following a similar pattern, such as self.D = {Bullet1: self.f_Bullet1, ...}. The rest of the code is the same.
PS2. The time/space complexity of the proposed solution is fairly good as well, with an average cost of O(1) per operation.
Use keyword arguments with defaults. E.g.
def add_bullet(sprite, start=default, direction=default, script=default, speed=default):
In the case of a straight bullet versus a curved bullet, I'd add two functions: add_bullet_straight and add_bullet_curved.
Overloading methods is tricky in Python. However, there could be usage of passing the dict, list or primitive variables.
I have tried something for my use cases, and this could help here to understand people to overload the methods.
Let's take your example:
A class overload method with call the methods from different class.
def add_bullet(sprite=None, start=None, headto=None, spead=None, acceleration=None):
Pass the arguments from the remote class:
add_bullet(sprite = 'test', start=Yes,headto={'lat':10.6666,'long':10.6666},accelaration=10.6}
Or
add_bullet(sprite = 'test', start=Yes, headto={'lat':10.6666,'long':10.6666},speed=['10','20,'30']}
So, handling is being achieved for list, Dictionary or primitive variables from method overloading.
Try it out for your code.
Plum supports it in a straightforward pythonic way. Copying an example from the README below.
from plum import dispatch
#dispatch
def f(x: str):
return "This is a string!"
#dispatch
def f(x: int):
return "This is an integer!"
>>> f("1")
'This is a string!'
>>> f(1)
'This is an integer!'
You can also try this code. We can try any number of arguments
# Finding the average of given number of arguments
def avg(*args): # args is the argument name we give
sum = 0
for i in args:
sum += i
average = sum/len(args) # Will find length of arguments we given
print("Avg: ", average)
# call function with different number of arguments
avg(1,2)
avg(5,6,4,7)
avg(11,23,54,111,76)
In Python, is there a way for an instance of an object to see the variable name it's assigned to? Take the following for example:
class MyObject(object):
pass
x = MyObject()
Is it possible for MyObject to see it's been assigned to a variable name x at any point? Like in it's __init__ method?
Yes, it is possible*. However, the problem is more difficult than it seems upon first glance:
There may be multiple names assigned to the same object.
There may be no names at all.
The same name(s) may refer to some other object(s) in a different namespace.
Regardless, knowing how to find the names of an object can sometimes be useful for debugging purposes - and here is how to do it:
import gc, inspect
def find_names(obj):
frame = inspect.currentframe()
for frame in iter(lambda: frame.f_back, None):
frame.f_locals
obj_names = []
for referrer in gc.get_referrers(obj):
if isinstance(referrer, dict):
for k, v in referrer.items():
if v is obj:
obj_names.append(k)
return obj_names
If you're ever tempted to base logic around the names of your variables, pause for a moment and consider if redesign/refactor of code could solve the problem. The need to recover an object's name from the object itself usually means that underlying data structures in your program need a rethink.
* at least in Cpython
As many others have said, it can't be done properly. However inspired by jsbueno's, I have an alternative to his solution.
Like his solution, I inspect the callers stack frame, which means it only works properly for Python-implemented callers (see note below). Unlike him, I inspect the bytecode of the caller directly (instead of loading and parsing the source code). Using Python 3.4+'s dis.get_instructions() this can be done with some hope of minimal compatibility. Though this is still some hacky code.
import inspect
import dis
def take1(iterator):
try:
return next(iterator)
except StopIteration:
raise Exception("missing bytecode instruction") from None
def take(iterator, count):
for x in range(count):
yield take1(iterator)
def get_assigned_name(frame):
"""Takes a frame and returns a description of the name(s) to which the
currently executing CALL_FUNCTION instruction's value will be assigned.
fn() => None
a = fn() => "a"
a, b = fn() => ("a", "b")
a.a2.a3, b, c* = fn() => ("a.a2.a3", "b", Ellipsis)
"""
iterator = iter(dis.get_instructions(frame.f_code))
for instr in iterator:
if instr.offset == frame.f_lasti:
break
else:
assert False, "bytecode instruction missing"
assert instr.opname.startswith('CALL_')
instr = take1(iterator)
if instr.opname == 'POP_TOP':
raise ValueError("not assigned to variable")
return instr_dispatch(instr, iterator)
def instr_dispatch(instr, iterator):
opname = instr.opname
if (opname == 'STORE_FAST' # (co_varnames)
or opname == 'STORE_GLOBAL' # (co_names)
or opname == 'STORE_NAME' # (co_names)
or opname == 'STORE_DEREF'): # (co_cellvars++co_freevars)
return instr.argval
if opname == 'UNPACK_SEQUENCE':
return tuple(instr_dispatch(instr, iterator)
for instr in take(iterator, instr.arg))
if opname == 'UNPACK_EX':
return (*tuple(instr_dispatch(instr, iterator)
for instr in take(iterator, instr.arg)),
Ellipsis)
# Note: 'STORE_SUBSCR' and 'STORE_ATTR' should not be possible here.
# `lhs = rhs` in Python will evaluate `lhs` after `rhs`.
# Thus `x.attr = rhs` will first evalute `rhs` then load `a` and finally
# `STORE_ATTR` with `attr` as instruction argument. `a` can be any
# complex expression, so full support for understanding what a
# `STORE_ATTR` will target requires decoding the full range of expression-
# related bytecode instructions. Even figuring out which `STORE_ATTR`
# will use our return value requires non-trivial understanding of all
# expression-related bytecode instructions.
# Thus we limit ourselfs to loading a simply variable (of any kind)
# and a arbitary number of LOAD_ATTR calls before the final STORE_ATTR.
# We will represents simply a string like `my_var.loaded.loaded.assigned`
if opname in {'LOAD_CONST', 'LOAD_DEREF', 'LOAD_FAST',
'LOAD_GLOBAL', 'LOAD_NAME'}:
return instr.argval + "." + ".".join(
instr_dispatch_for_load(instr, iterator))
raise NotImplementedError("assignment could not be parsed: "
"instruction {} not understood"
.format(instr))
def instr_dispatch_for_load(instr, iterator):
instr = take1(iterator)
opname = instr.opname
if opname == 'LOAD_ATTR':
yield instr.argval
yield from instr_dispatch_for_load(instr, iterator)
elif opname == 'STORE_ATTR':
yield instr.argval
else:
raise NotImplementedError("assignment could not be parsed: "
"instruction {} not understood"
.format(instr))
Note: C-implemented functions don't show up as Python stack frames and are thus hidden to this script. This will result in false positives. Consider Python function f() which calls a = g(). g() is C-implemented and calls b = f2(). When f2() tries to lookup up the assigned name, it will get a instead of b because the script is oblivious to C functions. (At least this is how I guess it will work :P )
Usage example:
class MyItem():
def __init__(self):
self.name = get_assigned_name(inspect.currentframe().f_back)
abc = MyItem()
assert abc.name == "abc"
No. Objects and names live in separate dimensions. One object can have many names during its lifetime, and it's impossible to determine which one might be the one you want. Even in here:
class Foo(object):
def __init__(self): pass
x = Foo()
two names denote the same object (self when __init__ runs, x in global scope).
Here is a simple function to achieve what you want, assuming you wish to retrieve the name of the variable where the instance is assigned from a method call :
import inspect
def get_instance_var_name(method_frame, instance):
parent_frame = method_frame.f_back
matches = {k: v for k,v in parent_frame.f_globals.items() if v is instance}
assert len(matches) < 2
return list(matches.keys())[0] if matches else None
Here is an usage example :
class Bar:
def foo(self):
print(get_instance_var_name(inspect.currentframe(), self))
bar = Bar()
bar.foo() # prints 'bar'
def nested():
bar.foo()
nested() # prints 'bar'
Bar().foo() # prints None
It can't be ordinarily done, though this can be achieved by using introspection and facilities meant for debugging a program. The code must run from a ".py" file though, and not from just compiled bytecode, or inside a zipped module - as it relies on the reading of the file source code, from within the method that should find about "where it is running".
The trick is to access the execution frame where the object was initialized from - with inspect.currentframe - the frame object has a "f_lineno" value which states the line number where the call to the object method (in this case, __init__) has been called. The function inspect.filename allows one to retrieve the source code for the file, and fetch the apropriate line number.
A naive parse then peek the part preeceding an "=" sign, and assumes it is the variable that will contain the object.
from inspect import currentframe, getfile
class A(object):
def __init__(self):
f = currentframe(1)
filename = getfile(f)
code_line = open(filename).readlines()[f.f_lineno - 1]
assigned_variable = code_line.split("=")[0].strip()
print assigned_variable
my_name = A()
other_name = A()
That won't work for multiple assignents, expressions composing with the object before the assignemtn is made, objects being appended to lists or added to dictionaries or sets, object instantiation in intialization of for loops, and God knows which more situations --
And have in mind that after the first attribution, the object could be referenced by any other variable as well.
Botton line: it is possible, but as a toy - it can't be used i production code -
just have the varibal name to be passed as a string during object initialization, just as one has to do when creating a collections.namedtuple
The "right way" to do it, if you are needing the name, is to explicitly pass the name to the object initialization, as a string parameter, like in:
class A(object):
def __init__(self, name):
self.name = name
x = A("x")
And still, if absolutely need to type the objects'name only once, there is another way - read on.
Due to Python's syntax, some special assignments, not using the "=" operator do allow an object to know it is assigned name. So, other statemtns that perform assignents in Python are the for, with, def and class keywords - It is possible to abuse this, as specfically a class creation and a function definition are assignment statements that create objects which "know" their names.
Let's focus on the def statement. It ordinarily creates a function. But using a decorator you can use "def" to create any kind of object - and have the name used for the function available to the constructor:
class MyObject(object):
def __new__(cls, func):
# Calls the superclass constructor and actually instantiates the object:
self = object.__new__(cls)
#retrieve the function name:
self.name = func.func_name
#returns an instance of this class, instead of a decorated function:
return self
def __init__(self, func):
print "My name is ", self.name
#and the catch is that you can't use "=" to create this object, you have to do:
#MyObject
def my_name(): pass
(This last way of doing it could be used in production code, unlike the one which resorts to reading the source file)
assuming this:
class MyObject(object):
pass
x = MyObject()
then you can search through the environment by the object's id, returning the key when there is a match.
keys = list(globals().keys()) # list all variable names
target = id(x) # find the id of your object
for k in keys:
value_memory_address = id(globals()[k]) # fetch id of every object
if value_memory_address == target:
print(globals()[k], k) # if there is a variable assigned to that id, then it is a variable that points to your object
I was independently working on this and have the following. It's not as comprehensive as driax's answer, but efficiently covers the case described and doesn't rely on searching for the object's id in global variables or parsing source code...
import sys
import dis
class MyObject:
def __init__(self):
# uses bytecode magic to find the name of the assigned variable
f = sys._getframe(1) # get stack frame of caller (depth=1)
# next op should be STORE_NAME (current op calls the constructor)
opname = dis.opname[f.f_code.co_code[f.f_lasti+2]]
if opname == 'STORE_NAME': # not all objects will be assigned a name
# STORE_NAME argument is the name index
namei = f.f_code.co_code[f.f_lasti+3]
self.name = f.f_code.co_names[namei]
else:
self.name = None
x = MyObject()
x.name == 'x'
In Python, is there a way for an instance of an object to see the variable name it's assigned to? Take the following for example:
class MyObject(object):
pass
x = MyObject()
Is it possible for MyObject to see it's been assigned to a variable name x at any point? Like in it's __init__ method?
Yes, it is possible*. However, the problem is more difficult than it seems upon first glance:
There may be multiple names assigned to the same object.
There may be no names at all.
The same name(s) may refer to some other object(s) in a different namespace.
Regardless, knowing how to find the names of an object can sometimes be useful for debugging purposes - and here is how to do it:
import gc, inspect
def find_names(obj):
frame = inspect.currentframe()
for frame in iter(lambda: frame.f_back, None):
frame.f_locals
obj_names = []
for referrer in gc.get_referrers(obj):
if isinstance(referrer, dict):
for k, v in referrer.items():
if v is obj:
obj_names.append(k)
return obj_names
If you're ever tempted to base logic around the names of your variables, pause for a moment and consider if redesign/refactor of code could solve the problem. The need to recover an object's name from the object itself usually means that underlying data structures in your program need a rethink.
* at least in Cpython
As many others have said, it can't be done properly. However inspired by jsbueno's, I have an alternative to his solution.
Like his solution, I inspect the callers stack frame, which means it only works properly for Python-implemented callers (see note below). Unlike him, I inspect the bytecode of the caller directly (instead of loading and parsing the source code). Using Python 3.4+'s dis.get_instructions() this can be done with some hope of minimal compatibility. Though this is still some hacky code.
import inspect
import dis
def take1(iterator):
try:
return next(iterator)
except StopIteration:
raise Exception("missing bytecode instruction") from None
def take(iterator, count):
for x in range(count):
yield take1(iterator)
def get_assigned_name(frame):
"""Takes a frame and returns a description of the name(s) to which the
currently executing CALL_FUNCTION instruction's value will be assigned.
fn() => None
a = fn() => "a"
a, b = fn() => ("a", "b")
a.a2.a3, b, c* = fn() => ("a.a2.a3", "b", Ellipsis)
"""
iterator = iter(dis.get_instructions(frame.f_code))
for instr in iterator:
if instr.offset == frame.f_lasti:
break
else:
assert False, "bytecode instruction missing"
assert instr.opname.startswith('CALL_')
instr = take1(iterator)
if instr.opname == 'POP_TOP':
raise ValueError("not assigned to variable")
return instr_dispatch(instr, iterator)
def instr_dispatch(instr, iterator):
opname = instr.opname
if (opname == 'STORE_FAST' # (co_varnames)
or opname == 'STORE_GLOBAL' # (co_names)
or opname == 'STORE_NAME' # (co_names)
or opname == 'STORE_DEREF'): # (co_cellvars++co_freevars)
return instr.argval
if opname == 'UNPACK_SEQUENCE':
return tuple(instr_dispatch(instr, iterator)
for instr in take(iterator, instr.arg))
if opname == 'UNPACK_EX':
return (*tuple(instr_dispatch(instr, iterator)
for instr in take(iterator, instr.arg)),
Ellipsis)
# Note: 'STORE_SUBSCR' and 'STORE_ATTR' should not be possible here.
# `lhs = rhs` in Python will evaluate `lhs` after `rhs`.
# Thus `x.attr = rhs` will first evalute `rhs` then load `a` and finally
# `STORE_ATTR` with `attr` as instruction argument. `a` can be any
# complex expression, so full support for understanding what a
# `STORE_ATTR` will target requires decoding the full range of expression-
# related bytecode instructions. Even figuring out which `STORE_ATTR`
# will use our return value requires non-trivial understanding of all
# expression-related bytecode instructions.
# Thus we limit ourselfs to loading a simply variable (of any kind)
# and a arbitary number of LOAD_ATTR calls before the final STORE_ATTR.
# We will represents simply a string like `my_var.loaded.loaded.assigned`
if opname in {'LOAD_CONST', 'LOAD_DEREF', 'LOAD_FAST',
'LOAD_GLOBAL', 'LOAD_NAME'}:
return instr.argval + "." + ".".join(
instr_dispatch_for_load(instr, iterator))
raise NotImplementedError("assignment could not be parsed: "
"instruction {} not understood"
.format(instr))
def instr_dispatch_for_load(instr, iterator):
instr = take1(iterator)
opname = instr.opname
if opname == 'LOAD_ATTR':
yield instr.argval
yield from instr_dispatch_for_load(instr, iterator)
elif opname == 'STORE_ATTR':
yield instr.argval
else:
raise NotImplementedError("assignment could not be parsed: "
"instruction {} not understood"
.format(instr))
Note: C-implemented functions don't show up as Python stack frames and are thus hidden to this script. This will result in false positives. Consider Python function f() which calls a = g(). g() is C-implemented and calls b = f2(). When f2() tries to lookup up the assigned name, it will get a instead of b because the script is oblivious to C functions. (At least this is how I guess it will work :P )
Usage example:
class MyItem():
def __init__(self):
self.name = get_assigned_name(inspect.currentframe().f_back)
abc = MyItem()
assert abc.name == "abc"
No. Objects and names live in separate dimensions. One object can have many names during its lifetime, and it's impossible to determine which one might be the one you want. Even in here:
class Foo(object):
def __init__(self): pass
x = Foo()
two names denote the same object (self when __init__ runs, x in global scope).
Here is a simple function to achieve what you want, assuming you wish to retrieve the name of the variable where the instance is assigned from a method call :
import inspect
def get_instance_var_name(method_frame, instance):
parent_frame = method_frame.f_back
matches = {k: v for k,v in parent_frame.f_globals.items() if v is instance}
assert len(matches) < 2
return list(matches.keys())[0] if matches else None
Here is an usage example :
class Bar:
def foo(self):
print(get_instance_var_name(inspect.currentframe(), self))
bar = Bar()
bar.foo() # prints 'bar'
def nested():
bar.foo()
nested() # prints 'bar'
Bar().foo() # prints None
It can't be ordinarily done, though this can be achieved by using introspection and facilities meant for debugging a program. The code must run from a ".py" file though, and not from just compiled bytecode, or inside a zipped module - as it relies on the reading of the file source code, from within the method that should find about "where it is running".
The trick is to access the execution frame where the object was initialized from - with inspect.currentframe - the frame object has a "f_lineno" value which states the line number where the call to the object method (in this case, __init__) has been called. The function inspect.filename allows one to retrieve the source code for the file, and fetch the apropriate line number.
A naive parse then peek the part preeceding an "=" sign, and assumes it is the variable that will contain the object.
from inspect import currentframe, getfile
class A(object):
def __init__(self):
f = currentframe(1)
filename = getfile(f)
code_line = open(filename).readlines()[f.f_lineno - 1]
assigned_variable = code_line.split("=")[0].strip()
print assigned_variable
my_name = A()
other_name = A()
That won't work for multiple assignents, expressions composing with the object before the assignemtn is made, objects being appended to lists or added to dictionaries or sets, object instantiation in intialization of for loops, and God knows which more situations --
And have in mind that after the first attribution, the object could be referenced by any other variable as well.
Botton line: it is possible, but as a toy - it can't be used i production code -
just have the varibal name to be passed as a string during object initialization, just as one has to do when creating a collections.namedtuple
The "right way" to do it, if you are needing the name, is to explicitly pass the name to the object initialization, as a string parameter, like in:
class A(object):
def __init__(self, name):
self.name = name
x = A("x")
And still, if absolutely need to type the objects'name only once, there is another way - read on.
Due to Python's syntax, some special assignments, not using the "=" operator do allow an object to know it is assigned name. So, other statemtns that perform assignents in Python are the for, with, def and class keywords - It is possible to abuse this, as specfically a class creation and a function definition are assignment statements that create objects which "know" their names.
Let's focus on the def statement. It ordinarily creates a function. But using a decorator you can use "def" to create any kind of object - and have the name used for the function available to the constructor:
class MyObject(object):
def __new__(cls, func):
# Calls the superclass constructor and actually instantiates the object:
self = object.__new__(cls)
#retrieve the function name:
self.name = func.func_name
#returns an instance of this class, instead of a decorated function:
return self
def __init__(self, func):
print "My name is ", self.name
#and the catch is that you can't use "=" to create this object, you have to do:
#MyObject
def my_name(): pass
(This last way of doing it could be used in production code, unlike the one which resorts to reading the source file)
assuming this:
class MyObject(object):
pass
x = MyObject()
then you can search through the environment by the object's id, returning the key when there is a match.
keys = list(globals().keys()) # list all variable names
target = id(x) # find the id of your object
for k in keys:
value_memory_address = id(globals()[k]) # fetch id of every object
if value_memory_address == target:
print(globals()[k], k) # if there is a variable assigned to that id, then it is a variable that points to your object
I was independently working on this and have the following. It's not as comprehensive as driax's answer, but efficiently covers the case described and doesn't rely on searching for the object's id in global variables or parsing source code...
import sys
import dis
class MyObject:
def __init__(self):
# uses bytecode magic to find the name of the assigned variable
f = sys._getframe(1) # get stack frame of caller (depth=1)
# next op should be STORE_NAME (current op calls the constructor)
opname = dis.opname[f.f_code.co_code[f.f_lasti+2]]
if opname == 'STORE_NAME': # not all objects will be assigned a name
# STORE_NAME argument is the name index
namei = f.f_code.co_code[f.f_lasti+3]
self.name = f.f_code.co_names[namei]
else:
self.name = None
x = MyObject()
x.name == 'x'