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Merge pull request #288 from gyermolenko/separate_version_specific_scripts_p2
Separate version specific scripts p2
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‎README.md

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@@ -28,7 +28,7 @@ __Structural Patterns__:
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| [composite](patterns/structural/composite.py) | lets clients treat individual objects and compositions uniformly |
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| [decorator](patterns/structural/decorator.py) | wrap functionality with other functionality in order to affect outputs |
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| [facade](patterns/structural/facade.py) | use one class as an API to a number of others |
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| [flyweight](patterns/structural/flyweight.py) | transparently reuse existing instances of objects with similar/identical state |
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| [flyweight](patterns/structural/flyweight__py3.py) | transparently reuse existing instances of objects with similar/identical state |
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| [front_controller](patterns/structural/front_controller.py) | single handler requests coming to the application |
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| [mvc](patterns/structural/mvc.py) | model<->view<->controller (non-strict relationships) |
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| [proxy](patterns/structural/proxy.py) | an object funnels operations to something else |
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| Pattern | Description |
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|:-------:| ----------- |
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| [blackboard](patterns/other/blackboard.py) | architectural model, assemble different sub-system knowledge to build a solution, AI approach - non gang of four pattern |
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| [blackboard](patterns/other/blackboard__py3.py) | architectural model, assemble different sub-system knowledge to build a solution, AI approach - non gang of four pattern |
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| [graph_search](patterns/other/graph_search.py) | graphing algorithms - non gang of four pattern |
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| [hsm](patterns/other/hsm/hsm.py) | hierarchical state machine - non gang of four pattern |
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‎patterns/other/blackboard.py renamed to ‎patterns/other/blackboard__py2.py

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‎patterns/other/blackboard__py3.py

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#!/usr/bin/env python
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# -*- coding: utf-8 -*-
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"""
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@author: Eugene Duboviy <eugene.dubovoy@gmail.com> | github.com/duboviy
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In Blackboard pattern several specialised sub-systems (knowledge sources)
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assemble their knowledge to build a possibly partial or approximate solution.
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In this way, the sub-systems work together to solve the problem,
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where the solution is the sum of its parts.
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https://en.wikipedia.org/wiki/Blackboard_system
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"""
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import abc
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import random
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class Blackboard(object):
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def __init__(self):
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self.experts = []
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self.common_state = {
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'problems': 0,
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'suggestions': 0,
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'contributions': [],
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'progress': 0, # percentage, if 100 -> task is finished
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}
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def add_expert(self, expert):
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self.experts.append(expert)
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class Controller(object):
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def __init__(self, blackboard):
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self.blackboard = blackboard
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def run_loop(self):
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while self.blackboard.common_state['progress'] < 100:
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for expert in self.blackboard.experts:
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if expert.is_eager_to_contribute:
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expert.contribute()
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return self.blackboard.common_state['contributions']
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class AbstractExpert(metaclass=abc.ABCMeta):
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def __init__(self, blackboard):
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self.blackboard = blackboard
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@property
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@abc.abstractmethod
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def is_eager_to_contribute(self):
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raise NotImplementedError('Must provide implementation in subclass.')
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@abc.abstractmethod
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def contribute(self):
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raise NotImplementedError('Must provide implementation in subclass.')
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class Student(AbstractExpert):
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@property
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def is_eager_to_contribute(self):
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return True
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def contribute(self):
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self.blackboard.common_state['problems'] += random.randint(1, 10)
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self.blackboard.common_state['suggestions'] += random.randint(1, 10)
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self.blackboard.common_state['contributions'] += [self.__class__.__name__]
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self.blackboard.common_state['progress'] += random.randint(1, 2)
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class Scientist(AbstractExpert):
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@property
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def is_eager_to_contribute(self):
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return random.randint(0, 1)
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def contribute(self):
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self.blackboard.common_state['problems'] += random.randint(10, 20)
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self.blackboard.common_state['suggestions'] += random.randint(10, 20)
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self.blackboard.common_state['contributions'] += [self.__class__.__name__]
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self.blackboard.common_state['progress'] += random.randint(10, 30)
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class Professor(AbstractExpert):
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@property
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def is_eager_to_contribute(self):
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return True if self.blackboard.common_state['problems'] > 100 else False
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def contribute(self):
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self.blackboard.common_state['problems'] += random.randint(1, 2)
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self.blackboard.common_state['suggestions'] += random.randint(10, 20)
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self.blackboard.common_state['contributions'] += [self.__class__.__name__]
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self.blackboard.common_state['progress'] += random.randint(10, 100)
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if __name__ == '__main__':
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blackboard = Blackboard()
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blackboard.add_expert(Student(blackboard))
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blackboard.add_expert(Scientist(blackboard))
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blackboard.add_expert(Professor(blackboard))
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c = Controller(blackboard)
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contributions = c.run_loop()
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from pprint import pprint
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pprint(contributions)
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### OUTPUT ###
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# ['Student',
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# 'Student',
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# 'Scientist',
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# 'Student',
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# 'Scientist',
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# 'Student',
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# 'Scientist',
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# 'Student',
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# 'Scientist',
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# 'Student',
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# 'Scientist',
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# 'Professor']

‎patterns/structural/flyweight.py renamed to ‎patterns/structural/flyweight__py2.py

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@@ -87,12 +87,9 @@ def __repr__(self):
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return "<Card: %s%s>" % (self.value, self.suit)
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def with_metaclass(meta, *bases):
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""" Provide python cross-version metaclass compatibility. """
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return meta("NewBase", bases, {})
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class Card2(object):
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__metaclass__ = FlyweightMeta
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class Card2(with_metaclass(FlyweightMeta)):
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def __init__(self, *args, **kwargs):
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# print('Init {}: {}'.format(self.__class__, (args, kwargs)))
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pass

‎patterns/structural/flyweight__py3.py

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#!/usr/bin/env python
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# -*- coding: utf-8 -*-
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"""
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*What is this pattern about?
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This pattern aims to minimise the number of objects that are needed by
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a program at run-time. A Flyweight is an object shared by multiple
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contexts, and is indistinguishable from an object that is not shared.
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The state of a Flyweight should not be affected by it's context, this
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is known as its intrinsic state. The decoupling of the objects state
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from the object's context, allows the Flyweight to be shared.
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*What does this example do?
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The example below sets-up an 'object pool' which stores initialised
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objects. When a 'Card' is created it first checks to see if it already
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exists instead of creating a new one. This aims to reduce the number of
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objects initialised by the program.
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*References:
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http://codesnipers.com/?q=python-flyweights
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*TL;DR80
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Minimizes memory usage by sharing data with other similar objects.
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"""
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import weakref
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class FlyweightMeta(type):
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def __new__(mcs, name, parents, dct):
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"""
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Set up object pool
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:param name: class name
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:param parents: class parents
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:param dct: dict: includes class attributes, class methods,
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static methods, etc
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:return: new class
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"""
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dct['pool'] = weakref.WeakValueDictionary()
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return super(FlyweightMeta, mcs).__new__(mcs, name, parents, dct)
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@staticmethod
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def _serialize_params(cls, *args, **kwargs):
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"""
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Serialize input parameters to a key.
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Simple implementation is just to serialize it as a string
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"""
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args_list = list(map(str, args))
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args_list.extend([str(kwargs), cls.__name__])
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key = ''.join(args_list)
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return key
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def __call__(cls, *args, **kwargs):
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key = FlyweightMeta._serialize_params(cls, *args, **kwargs)
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pool = getattr(cls, 'pool', {})
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instance = pool.get(key)
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if instance is None:
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instance = super(FlyweightMeta, cls).__call__(*args, **kwargs)
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pool[key] = instance
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return instance
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class Card(object):
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"""The object pool. Has builtin reference counting"""
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_CardPool = weakref.WeakValueDictionary()
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"""Flyweight implementation. If the object exists in the
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pool just return it (instead of creating a new one)"""
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def __new__(cls, value, suit):
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obj = Card._CardPool.get(value + suit)
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if not obj:
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obj = object.__new__(cls)
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Card._CardPool[value + suit] = obj
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obj.value, obj.suit = value, suit
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return obj
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# def __init__(self, value, suit):
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# self.value, self.suit = value, suit
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def __repr__(self):
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return "<Card: %s%s>" % (self.value, self.suit)
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class Card2(metaclass=FlyweightMeta):
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def __init__(self, *args, **kwargs):
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# print('Init {}: {}'.format(self.__class__, (args, kwargs)))
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pass
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if __name__ == '__main__':
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# comment __new__ and uncomment __init__ to see the difference
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c1 = Card('9', 'h')
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c2 = Card('9', 'h')
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print(c1, c2)
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print(c1 == c2, c1 is c2)
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print(id(c1), id(c2))
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c1.temp = None
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c3 = Card('9', 'h')
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print(hasattr(c3, 'temp'))
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c1 = c2 = c3 = None
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c3 = Card('9', 'h')
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print(hasattr(c3, 'temp'))
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# Tests with metaclass
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instances_pool = getattr(Card2, 'pool')
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cm1 = Card2('10', 'h', a=1)
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cm2 = Card2('10', 'h', a=1)
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cm3 = Card2('10', 'h', a=2)
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assert (cm1 == cm2) and (cm1 != cm3)
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assert (cm1 is cm2) and (cm1 is not cm3)
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assert len(instances_pool) == 2
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del cm1
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assert len(instances_pool) == 2
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del cm2
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assert len(instances_pool) == 1
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del cm3
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assert len(instances_pool) == 0
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### OUTPUT ###
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# (<Card: 9h>, <Card: 9h>)
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# (True, True)
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# (31903856, 31903856)
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# True
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# False

‎tests/structural/test_flyweight.py

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