接口

在 Python 中，鸭子类型（duck typing）是一种动态类型的风格。所谓鸭子类型，来自于 James Whitcomb Riley 的“鸭子测试”：

当看到一只鸟走起来像鸭子、游泳起来像鸭子、叫起来也像鸭子，那么这只鸟就可以被称为鸭子。

假设我们需要定义一个函数，这个函数使用一个类型为鸭子的参数，并调用它的走和叫方法。

在鸭子类型的语言中，这样的函数可以接受任何类型的对象，只要这个对象实现了走和叫的方法，否则就引发一个运行时错误。换句话说，任何拥有走和叫方法的参数都是合法的。

先看一个例子，父类：

class Leaf(object):
    def __init__(self, color="green"):
        self.color = color
    def fall(self):
        print "Splat!"

子类：

class MapleLeaf(Leaf):
    def fall(self):
        self.color = 'brown'
        super(MapleLeaf, self).fall()

新的类：

class Acorn(object):
    def fall(self):
        print "Plunk!"

这三个类都实现了 fall() 方法，因此可以这样使用：

objects = [Leaf(), MapleLeaf(), Acorn()]

for obj in objects:
    obj.fall()

Splat!
Splat!
Plunk!

这里 fall() 方法就一种鸭子类型的体现。

不仅方法可以用鸭子类型，属性也可以：

import numpy as np
from scipy.ndimage.measurements import label

class Forest(object):
    """ Forest can grow trees which eventually die."""
    def __init__(self, size=(150,150), p_sapling=0.0025):
        self.size = size
        self.trees = np.zeros(self.size, dtype=bool)
        self.p_sapling = p_sapling

    def __repr__(self):
        my_repr = "{}(size={})".format(self.__class__.__name__, self.size)
        return my_repr

    def __str__(self):
        return self.__class__.__name__

    @property
    def num_cells(self):
        """Number of cells available for growing trees"""
        return np.prod(self.size)

    @property
    def losses(self):
        return np.zeros(self.size)

    @property
    def tree_fraction(self):
        """
        Fraction of trees
        """
        num_trees = self.trees.sum()
        return float(num_trees) / self.num_cells

    def _rand_bool(self, p):
        """
        Random boolean distributed according to p, less than p will be True
        """
        return np.random.uniform(size=self.trees.shape) < p

    def grow_trees(self):
        """
        Growing trees.
        """
        growth_sites = self._rand_bool(self.p_sapling)
        self.trees[growth_sites] = True    

    def advance_one_step(self):
        """
        Advance one step
        """
        self.grow_trees()

class BurnableForest(Forest):
    """
    Burnable forest support fires
    """    
    def __init__(self, p_lightning=5.0e-6, **kwargs):
        super(BurnableForest, self).__init__(**kwargs)
        self.p_lightning = p_lightning        
        self.fires = np.zeros((self.size), dtype=bool)

    def advance_one_step(self):
        """
        Advance one step
        """
        super(BurnableForest, self).advance_one_step()
        self.start_fires()
        self.burn_trees()

    @property
    def losses(self):
        return self.fires

    @property
    def fire_fraction(self):
        """
        Fraction of fires
        """
        num_fires = self.fires.sum()
        return float(num_fires) / self.num_cells

    def start_fires(self):
        """
        Start of fire.
        """
        lightning_strikes = (self._rand_bool(self.p_lightning) & 
            self.trees)
        self.fires[lightning_strikes] = True

    def burn_trees(self):    
        pass

class SlowBurnForest(BurnableForest):
    def burn_trees(self):
        """
        Burn trees.
        """
        fires = np.zeros((self.size[0] + 2, self.size[1] + 2), dtype=bool)
        fires[1:-1, 1:-1] = self.fires
        north = fires[:-2, 1:-1]
        south = fires[2:, 1:-1]
        east = fires[1:-1, :-2]
        west = fires[1:-1, 2:]
        new_fires = (north | south | east | west) & self.trees
        self.trees[self.fires] = False
        self.fires = new_fires

class InstantBurnForest(BurnableForest):
    def burn_trees(self):
        # 起火点
        strikes = self.fires
        # 找到连通区域
        groves, num_groves = label(self.trees)
        fires = set(groves[strikes])
        self.fires.fill(False)
        # 将与着火点相连的区域都烧掉
        for fire in fires:
            self.fires[groves == fire] = True
        self.trees[self.fires] = False
        self.fires.fill(False)

测试：

forest = Forest()
b_forest = BurnableForest()
sb_forest = SlowBurnForest()
ib_forest = InstantBurnForest()

forests = [forest, b_forest, sb_forest, ib_forest]

losses_history = []

for i in xrange(1500):
    for fst in forests:
        fst.advance_one_step()
    losses_history.append(tuple(fst.losses.sum() for fst in forests))

显示结果：

import matplotlib.pyplot as plt
%matplotlib inline

plt.figure(figsize=(10,6))

plt.plot(losses_history)
plt.legend([f.__str__() for f in forests])

plt.show()

​