Python第五周 学习笔记（2）

def cache(fn):
    import inspect

    local_cache = {}

    def wrapper(*args, **kwargs):
        sig = inspect.signature(fn)
        params = sig.parameters

        param_names = list(params.keys())
        temp_dict = {}

        #处理位置参数
        for k, v in enumerate(args):
            temp_dict[param_names[k]] = v

        #更新关键字参数值
        temp_dict.update(kwargs)

        #更新默认值
        for k, v in params.items():
            temp_dict[k] = v.default

        #排序生成元组
        temp_tuple = tuple(sorted(temp_dict.items()))

        if temp_tuple not in local_cache.keys():
            local_cache[temp_tuple] = fn(*args, **kwargs)

        return local_cache[temp_tuple]
    return wrapper

import time
@cache
def add(x, y, z): 
    time.sleep(2)
    return x + y + z

import inspect
from datetime import datetime
def cache(duration):
    def _cache(fn):
        local_cache={}

        def wrapper(*args, **kwargs):

            def expire_cache(cache:dict):
                expire_list = []

                for k,(_,stamp)  in cache.items():
                    delta = (datetime.now().timestamp() - stamp)

                    if delta > duration:
                        expire_list.append(k)

                for k in expire_list:
                    cache.pop(k)

            expire_cache(local_cache)

            sig=inspect.signature(fn)
            params=sig.parameters

            param_names=list(params.keys())
            param_dict={}

            for k,v in enumerate(args):
                param_dict[param_names[k]] = v

            param_dict.update(kwargs)

            for k, v in params.items():
                if k not in param_dict.keys():
                    param_dict[k] = v.default

            param_keys=tuple(sorted(param_dict.items()))

            if param_keys not in local_cache.keys():
                local_cache[param_keys]=(fn(*args,**kwargs), datetime.now().timestamp())

            return local_cache[param_keys][0]
        return wrapper
    return _cache

def cmd_dispatcher(): #封装
    cmd_dict = {}

    def reg(cmd):
        def _reg(fn):
            cmd_dict[cmd] = fn
            return fn
        return _reg

    @reg('default_func')
    def default_func():
        print('default')
        return

    def dispatcher():
        while True:
            cmd = input('>>')
            if cmd == 'quit':
                return
            cmd_dict.get(cmd, default_func)()

    return reg, dispatcher #封装

reg, dispatcher = cmd_dispatcher() #封装&解构

@reg('add')
def add(): #add=reg('add')(add)
    print(1)
    return

dispatcher()

def show_tree(lst, unit_width=2):
    from math import log2, ceil

    length = len(lst)
    depth = ceil(log2(length + 1))
    width = 2 ** depth - 1
    index= 0

    for i in range(depth):
        for j in range(2 ** i):
            print('{:^{}}'.format(lst[index], width * unit_width), end = ' ' * unit_width)

            index += 1
            if index >= length:
                break
        width //= 2
        print()

from math import ceil, log2

#投影栅格实现
def print_tree(array):
    '''
    '''
    index = 1
    depth = ceil(log2(len(array)))
    sep = ' '
    for i in range(depth):
        offset = 2 ** i
        print(sep * (2 ** (depth - i -1) - 1), end = '')
        line = array[index : index + offset]
        for j, x in enumerate(line):
            print("{:>{}}".format(x, len(sep)), end = '')
            interval = 0 if i == 0 else 2 ** (depth - i) - 1
            if j < len(line) - 1:
                print(sep * interval, end = '')

        index += offset
        print()

def heap_sort(lst:list):
    '''
    堆排序

    :type lst: list
    :rtype: list
    '''
    length = len(lst)
    lst.insert(0,0) # 前插0为了索引和结点号能够对应上，索引不必加一，便于理解，输出时切片即可
    def heap_adjust(start, end):
        '''
        调整当前节点

        调整结点的起点在n//2,保证所有调整结点都有孩子结点
        :param end: 待比较个数
        :param start: 当前节点下标
        :rtype: None
        '''
        while 2 * start <= end: # 如果该结点下还有孩子则进入循环，否则跳出
            lchild_index = 2 * start #该结点号*2 为左孩子结点
            max_child_index = lchild_index #

            if lchild_index < end and lst[lchild_index + 1] > lst[lchild_index]: # 如果有右孩子并且右孩子比左孩子的数大，则更新最大值索引
                max_child_index = lchild_index + 1

            if lst[max_child_index] > lst[start]: #孩子结点比较完后与父结点比较，最大值放到父结点，并且下一次迭代的父结点是本次最大孩子索引
                lst[start], lst[max_child_index] = lst[max_child_index], lst[start]
                start = max_child_index
            else: # 如果父结点最大则直接跳出，因为排顶堆从编号最大的子树开始调整，即保证了本次最大孩子结点与其孩子结点已经形成了顶堆
                break

    for st in range(length//2, 0, -1): # 调整为大顶堆
        heap_adjust(st, length)

    for end in range(length, 1, -1): #sort排序 根结点与最后结点交换，每次迭代刨除最后结点，直至只剩根结点
        lst[1], lst[end] = lst[end], lst[1]
        heap_adjust(1, end - 1)

    return lst[1:]