I'm trying to return variable name, but i keep getting this:
<classes.man.man object at (some numbers (as example:0x03BDCA50))>
Below is my code:
from classes.man import man
def competition(guy1, guy2, counter1=0, counter2=0):
.......................
some *ok* manipulations
.......................
if counter1>counter2:
return guy1
bob = man(172, 'green')
bib = man(190, 'brown')
print(competition(bob , bib ))
Epilogue
If anyone want to, explain please what I can write instead of __class__ in example below to get variable name.
def __repr__(self):
return self.__class__.__name__
Anyway, thank you for all of your support
There are different ways to approach your problem.
The simplest I can fathom is if you can change the class man, make it accept an optional name in its __init__ and store it in the instance. This should look like this:
class man:
def __init__(number, color, name="John Doe"):
self.name = name
# rest of your code here
That way in your function you could just do with:
return guy1.name
Additionnally, if you want to go an extra step, you could define a __str__ method in your class man so that when you pass it to str() or print(), it shows the name instead:
# Inside class man
def __str__(self):
return self.name
That way your function could just do:
return guy1
And when you print the return value of your function it actually prints the name.
If you cannot alter class man, here is an extremely convoluted and costly suggestion, that could probably break depending on context:
import inspect
def competition(guy1, guy2, counter1=0, counter2=0):
guy1_name = ""
guy2_name = ""
for name, value in inspect.stack()[-1].frame.f_locals.items():
if value is guy1:
guy1_name = name
elif value is guy2:
guy2_name = name
if counter1 > counter2:
return guy1_name
elif counter2 > counter2:
return guy1_name
else:
return "Noone"
Valentin's answer - the first part of it at least (adding a name attribute to man) - is of course the proper, obvious solution.
Now wrt/ the second part (the inspect.stack hack), it's brittle at best - the "variables names" we're interested in might not necessarily be defined in the first parent frame, and FWIW they could as well just come from a dict etc...
Also, it's definitly not the competition() function's responsability to care about this (don't mix domain layer with presentation layer, thanks), and it's totally useless since the caller code can easily solve this part by itself:
def competition(guy1, guy2, counter1=0, counter2=0):
.......................
some *ok* manipulations
.......................
if counter1>counter2:
return guy1
def main():
bob = man(172, 'green')
bib = man(190, 'brown')
winner = competition(bob, bib)
if winner is bob:
print("bob wins")
elif winner is bib:
print("bib wins")
else:
print("tie!")
Python prints the location of class objects in memory if they are passed to the print() function as default. If you want a prettier output for a class you need to define the __repr__(self) function for that class which should return a string that is printed if an object is passed to print(). Then you can just return guy1
__repr__ is the method that defines the name in your case.
By default it gives you the object type information. If you want to print more apt name then you should override the __repr__ method
Check below code for instance
class class_with_overrided_repr:
def __repr__(self):
return "class_with_overrided_repr"
class class_without_overrided_repr:
pass
x = class_with_overrided_repr()
print x # class_with_overrided_repr
x = class_without_overrided_repr()
print x # <__main__.class_without_overrided_repr instance at 0x7f06002aa368>
Let me know if this what you want?
I have objects from various classes that work together to perform a certain task. The task requires a lot of parameters, provided by the user (through a configuration file). The parameters are used deep inside the system.
I have a choice of having the controller object read the configuration file, and then allocate the parameters as appropriate to the next layer of objects, and so on in each layer. But the only objects themselves know which parameters they need, so the controller object would need to learn a lot of detail about every other object.
The other choice is to bundle all the parameters into a collection, and pass the whole collection into every function call (equivalently, create a global object that stores them, and is accessible to everyone). This looks and feels ugly, and would cause a variety of minor technical issues (e.g., I can't allow two objects to use parameters with the same name; etc.)
What to do?
I have used the "global collection" alternative in the past.
If you are concerned with naming: how would you handle this in your config file? The way I see it, your global collection is a datastructure representing the same information you have in your config file, so if you have a way of resolving or avoiding name clashes in your cfg-file, you can do the same in your global collection.
I hope you don't feel like I'm thread-jacking you - what you're asking about is similar to what I was thinking about in terms of property aggregation to avoid the models you want to avoid.
I also nicked a bit of the declarative vibe that Elixir has turned me onto.
I'd be curious what the Python gurus of stack overflow think of it and what better alternatives there might be. I don't like big kwargs and if I can avoid big constructors I prefer to.
#!/usr/bin/python
import inspect
from itertools import chain, ifilter
from pprint import pprint
from abc import ABCMeta
class Property(object):
def __init__(self, value=None):
self._x = value
def __repr__(self):
return str(self._x)
def getx(self):
return self._x
def setx(self, value):
self._x = value
def delx(self):
del self._x
value = property(getx, setx, delx, "I'm the property.")
class BaseClass(object):
unique_baseclass_thing = Property()
def get_prop_tree(self):
mro = self.__class__.__mro__
r = []
for i in xrange( 0, len(mro) - 1 ):
child_prop_names = set(dir(mro[i]))
parent_prop_names = set(dir(mro[i+1]))
l_k = list( chain( child_prop_names - parent_prop_names ) )
l_n = [ (x, getattr(mro[i],x,None)) for x in l_k ]
l_p = list( ifilter(lambda y: y[1].__class__ == Property, l_n))
r.append(
(mro[i],
(dict
( l_p )
)
)
)
return r
def get_prop_list(self):
return list( chain(* [ x[1].items() for x in reversed( self.get_prop_tree() ) ] ) )
class SubClass(BaseClass):
unique_subclass_thing = Property(1)
class SubSubClass(SubClass):
unique_subsubclass_thing_one = Property("blah")
unique_subsubclass_thing_two = Property("foo")
if __name__ == '__main__':
a = SubSubClass()
for b in a.get_prop_tree():
print '---------------'
print b[0].__name__
for prop in b[1].keys():
print "\t", prop, "=", b[1][prop].value
print
for prop in a.get_prop_list():
When you run it..
SubSubClass
unique_subsubclass_thing_one = blah
unique_subsubclass_thing_two = foo
---------------
SubClass
unique_subclass_thing = 1
---------------
BaseClass
unique_baseclass_thing = None
unique_baseclass_thing None
unique_subclass_thing 1
unique_subsubclass_thing_one blah
unique_subsubclass_thing_two foo
Is there anyway to transform the following code in Java to Python's equivalence?
public class Animal{
public enum AnimalBreed{
Dog, Cat, Cow, Chicken, Elephant
}
private static final int Animals = AnimalBreed.Dog.ordinal();
private static final String[] myAnimal = new String[Animals];
private static Animal[] animal = new Animal[Animals];
public static final Animal DogAnimal = new Animal(AnimalBreed.Dog, "woff");
public static final Animal CatAnimal = new Animal(AnimalBreed.Cat, "meow");
private AnimalBreed breed;
public static Animal myDog (String name) {
return new Animal(AnimalBreed.Dog, name);
}
}
Translating this code directly would be a waste of time. The hardest thing when moving from Java to Python is giving up most of what you know. But the simple fact is that Python is not Java, and translating line by line won't work as you expect. It's better to translate algorithms rather than code, and let Python do what it's good at.
It's unclear to me what the desired semantics of your Java would be. I'm guessing you're sort of trying to model a collection of animals (species, not breeds, incidentally) and imbue a set of associated classes with the behavior that varies according to the type of animal (roughly speaking the sounds that each makes).
In Python the natural way to do this would be through meta programming. You create a class or a factory function which returns each of the classes by passing arguments into a template. Since functions and classes are first order object in Python they can be passed around like any other object. Since classes are themselves objects you can access their attributes using setattr (and its cousins: hasattr and getattr).
Here's a simple example:
#!/usr/bin/env python
def Animal(species, sound):
class meta: pass
def makeSound(meta, sound=sound):
print sound
setattr(meta, makeSound.__name__, makeSound)
def name(meta, myname=species):
return myname
setattr(meta, 'name', name)
return meta
if __name__ == '__main__':
animal_sounds = (('Dog', 'woof'),
('Cat', 'meow'),
('Cow', 'moo'),
('Chicken', 'cluck'),
('Elephant', 'eraunngh'))
menagerie = dict()
for animal, sound in animal_sounds:
menagerie[animal] = Animal(animal, sound)
for Beast in menagerie:
beast = Beast()
print beast.name(), ' says ',
beast.makeSound()
Dog = menagerie['Dog']
fido = Dog() # equivalent to fido = menagerie['Dog']()
fido.makeSound()
# prints "woof"
Cat = menagerie['Cat']
felix = Cat()
felix.makeSound()
Mouse = Animal('Mouse', 'squeak')
mickey = Mouse()
mouse.makeSound()
# prints "squeak"
This seems like a trite example but I hope it gets the point across. I can create a table (in this case a tuple of tuples) which provides the arguments which will be used to fill in the varying parameters/behavior of our classes. The classes returned by Animal are just like any other Python classes. I've tried to show that in the examples here.
This is not a line-for-line translation, but something in the ballpark:
class Animal(object):
animal_breeds = "Dog Cat Cow Chicken Elephant".split()
animals = {}
def __init__(self, breed, name):
self._breed = breed
self.name = name
Animal.animals[name] = self
#property
def breed(self):
return Animal.animal_breeds[self._breed]
#staticmethod
def myDog(name):
return Animal(Animal.AnimalBreed.Dog, name)
# add enumeration of Animal breeds to Animal class
class Constants(object): pass
Animal.AnimalBreed = Constants()
for i,b in enumerate(Animal.animal_breeds):
setattr(Animal.AnimalBreed, b, i)
# define some class-level constant animals
# (although "woff" and "meow" are not what I would expect
# for names of animals)
Animal.DogAnimal = Animal(Animal.AnimalBreed.Dog, "woff")
Animal.CatAnimal = Animal(Animal.AnimalBreed.Cat, "meow")
# this code would be in a separate module that would import this
# code using
# from animal import Animal
#
print Animal.myDog("Rex").breed
print Animal.animals.keys()
http://code.activestate.com/recipes/413486/ contains a lot of help on this topic. Be warned that deepcopy support probably doesn't work with it.
Related to this Stack Overflow question (C state-machine design), could you Stack Overflow folks share your Python state-machine design techniques with me (and the community)?
At the moment, I am going for an engine based on the following:
class TrackInfoHandler(object):
def __init__(self):
self._state="begin"
self._acc=""
## ================================== Event callbacks
def startElement(self, name, attrs):
self._dispatch(("startElement", name, attrs))
def characters(self, ch):
self._acc+=ch
def endElement(self, name):
self._dispatch(("endElement", self._acc))
self._acc=""
## ===================================
def _missingState(self, _event):
raise HandlerException("missing state(%s)" % self._state)
def _dispatch(self, event):
methodName="st_"+self._state
getattr(self, methodName, self._missingState)(event)
## =================================== State related callbacks
But I am sure there are tons of ways of going at it while leveraging Python's dynamic nature (e.g. dynamic dispatching).
I am after design techniques for the "engine" that receives the "events" and "dispatches" against those based on the "state" of the machine.
I don't really get the question. The State Design pattern is pretty clear. See the Design Patterns book.
class SuperState( object ):
def someStatefulMethod( self ):
raise NotImplementedError()
def transitionRule( self, input ):
raise NotImplementedError()
class SomeState( SuperState ):
def someStatefulMethod( self ):
actually do something()
def transitionRule( self, input ):
return NextState()
That's pretty common boilerplate, used in Java, C++, Python (and I'm sure other languages, also).
If your state transition rules happen to be trivial, there are some optimizations to push the transition rule itself into the superclass.
Note that we need to have forward references, so we refer to classes by name, and use eval to translate a class name to an actual class. The alternative is to make the transition rules instance variables instead of class variables and then create the instances after all the classes are defined.
class State( object ):
def transitionRule( self, input ):
return eval(self.map[input])()
class S1( State ):
map = { "input": "S2", "other": "S3" }
pass # Overrides to state-specific methods
class S2( State ):
map = { "foo": "S1", "bar": "S2" }
class S3( State ):
map = { "quux": "S1" }
In some cases, your event isn't as simple as testing objects for equality, so a more general transition rule is to use a proper list of function-object pairs.
class State( object ):
def transitionRule( self, input ):
next_states = [ s for f,s in self.map if f(input) ]
assert len(next_states) >= 1, "faulty transition rule"
return eval(next_states[0])()
class S1( State ):
map = [ (lambda x: x == "input", "S2"), (lambda x: x == "other", "S3" ) ]
class S2( State ):
map = [ (lambda x: "bar" <= x <= "foo", "S3"), (lambda x: True, "S1") ]
Since the rules are evaluated sequentially, this allows a "default" rule.
In the April, 2009 issue of Python Magazine, I wrote an article on embedding a State DSL within Python, using pyparsing and imputil. This code would allow you to write the module trafficLight.pystate:
# trafficLight.pystate
# define state machine
statemachine TrafficLight:
Red -> Green
Green -> Yellow
Yellow -> Red
# define some class level constants
Red.carsCanGo = False
Yellow.carsCanGo = True
Green.carsCanGo = True
Red.delay = wait(20)
Yellow.delay = wait(3)
Green.delay = wait(15)
and the DSL compiler would create all the necessary TrafficLight, Red, Yellow, and Green classes, and the proper state transition methods. Code could call these classes using something like this:
import statemachine
import trafficLight
tl = trafficLight.Red()
for i in range(6):
print tl, "GO" if tl.carsCanGo else "STOP"
tl.delay()
tl = tl.next_state()
(Unfortunately, imputil has been dropped in Python 3.)
There is this design pattern for using decorators to implement state machines. From the description on the page:
Decorators are used to specify which methods are the event handlers for the class.
There is example code on the page as well (it is quite long so I won't paste it here).
I also was not happy with the current options for state_machines so I wrote the state_machine library.
You can install it by pip install state_machine and use it like so:
#acts_as_state_machine
class Person():
name = 'Billy'
sleeping = State(initial=True)
running = State()
cleaning = State()
run = Event(from_states=sleeping, to_state=running)
cleanup = Event(from_states=running, to_state=cleaning)
sleep = Event(from_states=(running, cleaning), to_state=sleeping)
#before('sleep')
def do_one_thing(self):
print "{} is sleepy".format(self.name)
#before('sleep')
def do_another_thing(self):
print "{} is REALLY sleepy".format(self.name)
#after('sleep')
def snore(self):
print "Zzzzzzzzzzzz"
#after('sleep')
def big_snore(self):
print "Zzzzzzzzzzzzzzzzzzzzzz"
person = Person()
print person.current_state == person.sleeping # True
print person.is_sleeping # True
print person.is_running # False
person.run()
print person.is_running # True
person.sleep()
# Billy is sleepy
# Billy is REALLY sleepy
# Zzzzzzzzzzzz
# Zzzzzzzzzzzzzzzzzzzzzz
print person.is_sleeping # True
I think S. Lott's answer is a much better way to implement a state machine, but if you still want to continue with your approach, using (state,event) as the key for your dict is better. Modifying your code:
class HandlerFsm(object):
_fsm = {
("state_a","event"): "next_state",
#...
}
It probably depends on how complex your state machine is. For simple state machines, a dict of dicts (of event-keys to state-keys for DFAs, or event-keys to lists/sets/tuples of state-keys for NFAs) will probably be the simplest thing to write and understand.
For more complex state machines, I've heard good things about SMC, which can compile declarative state machine descriptions to code in a wide variety of languages, including Python.
The following code is a really simple solution. The only interesting part is:
def next_state(self,cls):
self.__class__ = cls
All the logic for each state is contained in a separate class. The 'state' is changed by replacing the '__class__' of the running instance.
#!/usr/bin/env python
class State(object):
call = 0 # shared state variable
def next_state(self,cls):
print '-> %s' % (cls.__name__,),
self.__class__ = cls
def show_state(self,i):
print '%2d:%2d:%s' % (self.call,i,self.__class__.__name__),
class State1(State):
__call = 0 # state variable
def __call__(self,ok):
self.show_state(self.__call)
self.call += 1
self.__call += 1
# transition
if ok: self.next_state(State2)
print '' # force new line
class State2(State):
__call = 0
def __call__(self,ok):
self.show_state(self.__call)
self.call += 1
self.__call += 1
# transition
if ok: self.next_state(State3)
else: self.next_state(State1)
print '' # force new line
class State3(State):
__call = 0
def __call__(self,ok):
self.show_state(self.__call)
self.call += 1
self.__call += 1
# transition
if not ok: self.next_state(State2)
print '' # force new line
if __name__ == '__main__':
sm = State1()
for v in [1,1,1,0,0,0,1,1,0,1,1,0,0,1,0,0,1,0,0]:
sm(v)
print '---------'
print vars(sm
Result:
0: 0:State1 -> State2
1: 0:State2 -> State3
2: 0:State3
3: 1:State3 -> State2
4: 1:State2 -> State1
5: 1:State1
6: 2:State1 -> State2
7: 2:State2 -> State3
8: 2:State3 -> State2
9: 3:State2 -> State3
10: 3:State3
11: 4:State3 -> State2
12: 4:State2 -> State1
13: 3:State1 -> State2
14: 5:State2 -> State1
15: 4:State1
16: 5:State1 -> State2
17: 6:State2 -> State1
18: 6:State1
---------
{'_State1__call': 7, 'call': 19, '_State3__call': 5, '_State2__call': 7}
I think that the tool PySCXML needs a closer look too.
This project uses the W3C definition: State Chart XML (SCXML): State Machine Notation for Control Abstraction
SCXML provides a generic state-machine based execution environment based on CCXML and Harel State Tables
Currently, SCXML is a working draft; but chances are quite high that it is getting a W3C recommendation soon (it is the 9th draft).
Another interesting point to highlight is that there is an Apache Commons project aimed at creating and maintaining a Java SCXML engine capable of executing a state machine defined using a SCXML document, while abstracting out the environment interfaces...
And for certain other tools, supporting this technology will emerge in the future when SCXML is leaving its draft-status...
I wouldn't think to reach for a finite state machine for handling XML. The usual way to do this, I think, is to use a stack:
class TrackInfoHandler(object):
def __init__(self):
self._stack=[]
## ================================== Event callbacks
def startElement(self, name, attrs):
cls = self.elementClasses[name]
self._stack.append(cls(**attrs))
def characters(self, ch):
self._stack[-1].addCharacters(ch)
def endElement(self, name):
e = self._stack.pop()
e.close()
if self._stack:
self._stack[-1].addElement(e)
For each kind of element, you just need a class that supports the addCharacters, addElement, and close methods.
EDIT: To clarify, yes I do mean to argue that finite state machines are usually the wrong answer, that as a general-purpose programming technique they're rubbish and you should stay away.
There are a few really well-understood, cleanly-delineated problems for which FSMs are a nice solution. lex, for example, is good stuff.
That said, FSMs typically don't cope well with change. Suppose someday you want to add a bit of state, perhaps a "have we seen element X yet?" flag. In the code above, you add a boolean attribute to the appropriate element class and you're done. In a finite state machine, you double the number of states and transitions.
Problems that require finite state at first very often evolve to require even more state, like maybe a number, at which point either your FSM scheme is toast, or worse, you evolve it into some kind of generalized state machine, and at that point you're really in trouble. The further you go, the more your rules start to act like code—but code in a slow interpreted language you invented that nobody else knows, for which there's no debugger and no tools.
I would definitely not recommend implementing such a well known pattern yourself. Just go for an open source implementation like transitions and wrap another class around it if you need custom features. In this post I explain why I prefer this particular implementation and its features.
Other related projects:
http://fsme.sourceforge.net/
https://code.google.com/p/visio2python/
You can paint state-machine and then use it in your code.
Here is a solution for "statefull objects" I've come up with, but it is rather inefficient for your intended purpose because state changes are relatively expensive. However, it may work well for objects which change state infrequently or undergo only a bounded number of state changes. The advantage is that once the state is changed, there is no redundant indirection.
class T:
"""
Descendant of `object` that rectifies `__new__` overriding.
This class is intended to be listed as the last base class (just
before the implicit `object`). It is a part of a workaround for
* https://bugs.python.org/issue36827
"""
#staticmethod
def __new__(cls, *_args, **_kwargs):
return object.__new__(cls)
class Stateful:
"""
Abstract base class (or mixin) for "stateful" classes.
Subclasses must implement `InitState` mixin.
"""
#staticmethod
def __new__(cls, *args, **kwargs):
# XXX: see https://stackoverflow.com/a/9639512
class CurrentStateProxy(cls.InitState):
#staticmethod
def _set_state(state_cls=cls.InitState):
__class__.__bases__ = (state_cls,)
class Eigenclass(CurrentStateProxy, cls):
__new__ = None # just in case
return super(__class__, cls).__new__(Eigenclass, *args, **kwargs)
# XXX: see https://bugs.python.org/issue36827 for the reason for `T`.
class StatefulThing(Stateful, T):
class StateA:
"""First state mixin."""
def say_hello(self):
self._say("Hello!")
self.hello_count += 1
self._set_state(self.StateB)
return True
def say_goodbye(self):
self._say("Another goodbye?")
return False
class StateB:
"""Second state mixin."""
def say_hello(self):
self._say("Another hello?")
return False
def say_goodbye(self):
self._say("Goodbye!")
self.goodbye_count += 1
self._set_state(self.StateA)
return True
# This one is required by `Stateful`.
class InitState(StateA):
"""Third state mixin -- the initial state."""
def say_goodbye(self):
self._say("Why?")
return False
def __init__(self, name):
self.name = name
self.hello_count = self.goodbye_count = 0
def _say(self, message):
print("{}: {}".format(self.name, message))
def say_hello_followed_by_goodbye(self):
self.say_hello() and self.say_goodbye()
# ----------
# ## Demo ##
# ----------
if __name__ == "__main__":
t1 = StatefulThing("t1")
t2 = StatefulThing("t2")
print("> t1, say hello.")
t1.say_hello()
print("> t2, say goodbye.")
t2.say_goodbye()
print("> t2, say hello.")
t2.say_hello()
print("> t1, say hello.")
t1.say_hello()
print("> t1, say hello followed by goodbye.")
t1.say_hello_followed_by_goodbye()
print("> t2, say goodbye.")
t2.say_goodbye()
print("> t2, say hello followed by goodbye.")
t2.say_hello_followed_by_goodbye()
print("> t1, say goodbye.")
t1.say_goodbye()
print("> t2, say hello.")
t2.say_hello()
print("---")
print( "t1 said {} hellos and {} goodbyes."
.format(t1.hello_count, t1.goodbye_count) )
print( "t2 said {} hellos and {} goodbyes."
.format(t2.hello_count, t2.goodbye_count) )
# Expected output:
#
# > t1, say hello.
# t1: Hello!
# > t2, say goodbye.
# t2: Why?
# > t2, say hello.
# t2: Hello!
# > t1, say hello.
# t1: Another hello?
# > t1, say hello followed by goodbye.
# t1: Another hello?
# > t2, say goodbye.
# t2: Goodbye!
# > t2, say hello followed by goodbye.
# t2: Hello!
# t2: Goodbye!
# > t1, say goodbye.
# t1: Goodbye!
# > t2, say hello.
# t2: Hello!
# ---
# t1 said 1 hellos and 1 goodbyes.
# t2 said 3 hellos and 2 goodbyes.
I've posted a "request for remarks" here.