#函数式编程的一个特点就是，允许把函数本身作为参数传入另一个函数，还允许返回一个函数
#编写高阶函数，就是让函数的参数能够接收别的函数

def add(x, y, f):
    return f(x) + f(y)

print(add(-1, 1, abs))

def f(x):
    return x * x
r = map(f, [1, 2, 3, 4, 5])
print(list(r))
#map()函数接收两个参数，一个是函数，一个是Iterable，
#map将传入的函数依次作用到序列的每个元素，并把结果作为新的Iterator返回

#reduce把一个函数作用在一个序列[x1, x2, x3, ...]上，这个函数必须接收两个参数，
#reduce把结果继续和序列的下一个元素做累积计算
from functools import reduce

def add(x, y):
    return x + y

print(reduce(add, [1, 3, 5, 7, 9]))

#13579
def fn(x, y):
    return x * 10 + y
print(reduce(fn, [1, 3, 5, 7, 9]))

def char2num(s):
    return {‘0‘: 0, ‘1‘: 1, ‘2‘: 2, ‘3‘: 3, ‘4‘: 4, ‘5‘: 5, ‘6‘: 6, ‘7‘: 7, ‘8‘: 8, ‘9‘: 9}[s]

print(reduce(fn, map(char2num, ‘13579‘)))


#def str2int(s):
#    def fn(x, y):
#        return x * 10 + y
#    def char2num(s):
#        return {‘0‘: 0, ‘1‘: 1, ‘2‘: 2, ‘3‘: 3, ‘4‘: 4, ‘5‘: 5, ‘6‘: 6, ‘7‘: 7, ‘8‘: 8, ‘9‘: 9}[s]
#    return reduce(fn, map(char2num, s))

#def char2num(s):
#    return {‘0‘: 0, ‘1‘: 1, ‘2‘: 2, ‘3‘: 3, ‘4‘: 4, ‘5‘: 5, ‘6‘: 6, ‘7‘: 7, ‘8‘: 8, ‘9‘: 9}[s]
#
#def str2int(s):
#    return reduce(lambda x, y: x * 10 + y, map(char2num, s))

print(sorted([36, 5, -12, 9, -21]))

print(sorted([36, 5, -12, 9, -21], key=abs))

print(sorted([‘bob‘, ‘about‘, ‘Zoo‘, ‘Credit‘]))
print(sorted([‘bob‘, ‘about‘, ‘Zoo‘, ‘Credit‘], key=str.lower, reverse=True))

def lazy_sum(*args):
    def sum():
        ax = 0
        for n in args:
            ax = ax + n
        return ax
    return sum

f = lazy_sum(1, 3, 5, 7, 9)
print(f())


def count():
    def f(j):
        def g():
            return j*j
        return g
    fs = []
    for i in range(1, 4):
        fs.append(f(i)) # f(i)立刻被执行，因此i的当前值被传入f()
    return fs

f1, f2, f3 = count()

print(f1())
print(f2())
print(f3())

#匿名函数

print(list(map(lambda x: x*x, [1, 2, 3])))

#匿名函数有个限制，就是只能有一个表达式，不用写return，返回值就是该表达式的结果
#def f(x):
#    return x * x

#def log(func):
#    @functools.wraps(func)
#    def wrapper(*args, **kw):
#        print(‘call %s():‘ % func.__name__)
#        return func(*args, **kw)
#    return wrapper

import functools

def log(text):
    def decorator(func):
        @functools.wraps(func)
        def wrapper(*args, **kw):
            print(‘%s %s():‘ % (text, func.__name__))
            return func(*args, **kw)
        return wrapper
    return decorator

#@log
@log(‘execute‘)
def now():
    print(‘2015-3-25‘)

f = now
f()

print(now.__name__)
print(f.__name__)

#把@log放到now()函数的定义处，相当于执行了语句：now = log(now)

print(int(‘12345‘))
print(int(‘12345‘, base=8))
print(int(‘12345‘, 16))

#def int2(x, base=2):
#    return int(x, base)

#print(int2(‘10000000000‘)) #转换二进制


import functools

int2 = functools.partial(int, base=2)
print(int2(‘100000‘))
print(int2(‘100000‘, base=10))

#创建偏函数时，实际上可以接收函数对象、*args和**kw这3个参数

max2 = functools.partial(max, 10)
print(max2(2))