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import itchat itchat.auto_login(hotReload=True)#hotReload=True for room in itchat.get_chatrooms(update=True)[0:]: chatroom=itchat.update_chatroom(room['UserName'],detailedMember=True) path="C:/Users/Administrator/Documents/group/"+room['NickName']+'.txt' try: with open(path,'a',encoding='utf-8') as f: i=1 for friend in chatroom['MemberList']: if friend['DisplayName']=='': friend['DisplayName']='No_DisplayName' f.write(repr(i)+'.'+friend['DisplayName']+' '+friend['NickName']+'\n') i+=1 except Exception as e: print(repr(e)) itchat.run() |
月份：2018年4月
非修改性序列算法
该算法不改动容器中元素的次序，也不改动元素值。一般通过input迭代器和forward迭代器完成工作，可用于所有的标准容器。
一.for_each算法：for_each(iterator begin,iterator end,proc op)
该算法对区间[begin，end)中的每个元素调用进程op
①不修改元素
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#include <iostream> #include<vector> #include<algorithm> using namespace std; void print(int a) { cout << a << endl; } int main() { vector<int>vi; for (int i = 1; i < 10; i += 2) { vi.push_back(i); } for_each(vi.begin(), vi.end(), [](int b) //lambda { cout <<b << endl; } ); for_each(vi.begin(),vi.end(),print);//或者 return 0; } /* 1 3 5 7 9 */ |
②修改元素
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#include <iostream> #include<vector> #include<algorithm> using namespace std; void print(int a) { cout << a << endl; } int main() { vector<int>vi; for (int i = 1; i < 10; i += 2) { vi.push_back(i); } for_each(vi.begin(), vi.end(), [](int &b) //引用 { b+=100; } ); for_each(vi.begin(),vi.end(),print); return 0; } /* 101 103 105 107 109 */ |
③使用其返回值：例如返回sum求和
二.元素计数
count(Iterator start,Iterator end,value)
count(Iterator start,Iterator end,Condition)
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#include <iostream> #include<vector> #include<functional>//bind2nd() #include<algorithm> using namespace std; bool isEven(int elem) { return elem % 2 == 0; } int main() { vector<int>vi; for (int i = 0; i < 10; i += 2) { vi.push_back(i); } vi.push_back(5); cout << count_if(vi.begin(),vi.end(),isEven)<<endl;//统计偶数的个数：5 cout << count_if(vi.begin(), vi.end(), bind2nd(greater<int>(), 2));//统计比2大的数的个数：4 return 0; } |
三.最大值最小值
最小值：
Iterator min_element(Iterator beg,Iterator end);
Iterator min_element(Iterator beg,Iterator end,comFunc op);
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#include <iostream> #include<vector> #include<algorithm> using namespace std; bool absless(int elem1,int elem2) { return abs(elem1) < abs(elem2); } int main() { vector<int>vi; for (int i = -8; i < 10; i += 2) { vi.push_back(i); } vi.push_back(5); cout << *min_element(vi.begin(), vi.end()) << endl;//输出向量中最小的元素：-8 cout << *min_element(vi.begin(), vi.end(),absless) << endl;//输出向量绝对值中最小的元素：0 return 0; } |
四.搜寻元素
1.搜寻第一个匹配元素
Iterator find(Iterator beg,Iterator end，value);
Iterator find_if(Iterator beg,Iterator end，value，ConditionFunc);
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#include <iostream> #include<vector> #include<functional> #include<algorithm> using namespace std; int main() { vector<int>vi; for (int i = -8; i < 10; i += 2) { vi.push_back(i); } vi.push_back(5); cout << *find(vi.begin(),vi.end(),0) << endl;//查找0，失败则返回end迭代器 cout << *find_if(vi.begin(), vi.end(),bind2nd(greater<int>(),2)) << endl;//输出向量第一个大于2的元素：4 return 0; } |
2.搜寻前n个连续匹配值
Iterator search_n(Iterator beg,Iterator end，Size count,value);返回连续count等于value的元素的起始位置
Iterator search_n(Iterator beg,Iterator end，Size count,value,BinaryPredicate op);返回连续count满足op的元素的起始位置
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#include <iostream> #include<vector> #include<functional> #include<algorithm> using namespace std; int main() { vector<int>vi; vector<int>::iterator it; for (int i = -2; i < 10; i += 2) { vi.push_back(i); } vi.push_back(5); vi.push_back(5); vi.push_back(5); it = search_n(vi.begin(), vi.end(), 3, 5); cout << distance(vi.begin(),it) << endl;//返回3个连续等于5的元素的起始位置：6 it = search_n(vi.begin(), vi.end(), 3, 4, greater<int>()); cout << distance(vi.begin(), it) << endl;//返回3个连续大于4的元素的起始位置：4 return 0; } |
3.搜寻第一个子区间
Iterator search(Iterator1 beg,Iterator1 end，Iterator2 beg,Iterator2 end)；iterator1原串，iterator子串
Iterator search(Iterator1 beg,Iterator1 end，Iterator2 beg,Iterator2 end,BinaryPredicate op)；
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#include <iostream> #include<vector> #include<functional> #include<algorithm> using namespace std; bool cmp(int elem,bool even) { if (even) { return elem % 2 == 0; } else return elem % 2 == 1; } int main() { vector<int>v1,v2; vector<int>::iterator it; bool check[] = { true,false,true }; for (int i = -2; i < 10; i += 2) { v1.push_back(i); } for (int i = 4; i < 10; i += 2) { v2.push_back(i); } it = search(v1.begin(),v1.end(),v2.begin(),v2.end()); cout << distance(v1.begin(),it) << endl;//返回子串在原串的起始位置：3 it = search(v1.begin(),v1.end(),check,check+3,cmp); cout << distance(v1.begin(),it) << endl;//返回满足偶奇偶顺序的子串起始位置：6（即没有） return 0; } |
4.搜寻最后一个子区间（类似于3）
Iterator find_end(Iterator1 beg,Iterator1 end，Iterator2 beg,Iterator2 end)；iterator1原串，iterator子串
Iterator find_end(Iterator1 beg,Iterator1 end，Iterator2 beg,Iterator2 end,BinaryPredicate op)；
5.搜寻某些元素的第一次出现位置(类似于search)
Iterator find_first_of(Iterator1 beg,Iterator1 end，Iterator2 beg,Iterator2 end)；iterator1原串，iterator子串
Iterator find_first_of(Iterator1 beg,Iterator1 end，Iterator2 beg,Iterator2 end,BinaryPredicate op)；
6.搜寻两个连续相等的元素（或者连续满足条件的）
Iterator adjacent_find(Iterator beg,Iterator end)；
Iterator adjacent_find(Iterator beg,Iterator end,Predicate op)；
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#include <iostream> #include<vector> #include<functional> #include<algorithm> using namespace std; bool doubles(int elem1,int elem2) { return elem1 * 2 == elem2; } int main() { vector<int>v1; vector<int>::iterator it; v1.push_back(-2); for (int i = -2; i < 10; i += 2) { v1.push_back(i); } it = adjacent_find(v1.begin(),v1.end()); cout << distance(v1.begin(),it) << endl;//返回连续相等元素的起始位置：3 it = adjacent_find(v1.begin(),v1.end(),doubles); cout << distance(v1.begin(),it) << endl;//返回第二个元素是第一个元素两倍起始位置：3 return 0; } |
HTML简介
1.基本结构
<html></html>：html文档被其包裹
<head></head>：html头文件标记，不在浏览器中显示
<title></title>:html文件标题，网页主题
<body></body>:网页的主体部分，在浏览器中显示
<meta></meta>:网页的元信息，提供有关页面的原信息，比如针对搜索引擎和更新频度的描述和关键词。meta标记必须放在head元素里面
2.文档设置标记
多线程
一、创建
1.用threading模块创建多线程
①把一个函数传入并创建Thread实例，然后用start启动
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import random,time,threading def thread_run(urls): print('Current %s is running'%threading.current_thread().name) for url in urls: print('%s---->%s'%(threading.current_thread().name,url)) time.sleep(random.random()) print('%s ended.'%threading.current_thread().name) print('%s is running '%threading.current_thread().name) t1=threading.Thread(target=thread_run,name='Thread_1',args=(['url_1','url_2','url_3'],)) t2=threading.Thread(target=thread_run,name='Thread_2',args=(['url_4','url_5','url_6'],)) t1.start() t2.start() t1.join() t2.join() print('%s ended.'%threading.current_thread().name) # MainThread is running # Current Thread_1 is running # Thread_1---->url_1 # Current Thread_2 is running # Thread_2---->url_4 # Thread_2---->url_5 # Thread_2---->url_6 # Thread_2 ended. # Thread_1---->url_2 # Thread_1---->url_3 # Thread_1 ended. # MainThread ended. |
②从threading.Thread继承并创建线程类，然后重写__init__() 和run()方法
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import random, time, threading class myThread(threading.Thread): def __init__(self, name, urls): super().__init__(name=name) self.urls = urls def run(self): print('Current %s is running' % threading.current_thread().name) for url in self.urls: print('%s---->%s' % (threading.current_thread().name, url)) time.sleep(random.random()) print('%s ended.' % threading.current_thread().name) print('%s is running' % threading.current_thread().name) t1 = myThread(name='Thread_1', urls=['url_1', 'url_2', 'url_3']) t2 = myThread(name='Thread_2', urls=['url_4', 'url_5', 'url_6']) t1.start() t2.start() t1.join() t2.join() # MainThread is running # Current Thread_1 is running # Thread_1---->url_1 # Current Thread_2 is running # Thread_2---->url_4 # Thread_2---->url_5 # Thread_2---->url_6 # Thread_1---->url_2 # Thread_2 ended. # Thread_1---->url_3 # Thread_1 ended. # MainThread ended. |
2.使用threading.Thread
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from threading import Thread |
二、实例
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def main(cand): pass t1 = Thread(target=main, args=(cand_list_key[0],)) threads.append(t1) t2 = Thread(target=main, args=(cand_list_key[1],)) threads.append(t2) t3 = Thread(target=main, args=(cand_list_key[2],)) threads.append(t3) t4 = Thread(target=main, args=(cand_list_key[3],)) threads.append(t4) for t in threads: t.start() for t in threads: # 所有子线程结束了，再继续主线程 t.join() print('OK!') |
多线程读写文件时，需要分块操作，每一块开一个线程。
ref：Python多线程循环
Python 机器学习库 NumPy 教程
一.介绍
NumPy的数组类是ndarray，它有一个别名是 numpy.array，但这与Python标准库的array.array并不一样。后者仅仅是一个一维数组。而ndarray具有以下的属性：
ndarray.ndim：数组的维数。在Python世界中，维数称之为rankndarray.shape：数组的维度。这是一系列数字，长度由数组的维度（ndim）决定。例如：长度为n的一维数组的shape是n。一个n行m列的矩阵的shape是n,mndarray.size：数组中所有元素的数量ndarray.dtype：数组中元素的类型，例如numpy.int32,numpy.int16或者numpy.float64ndarray.itemsize：数组中每个元素的大小，单位为字节ndarray.data：存储数组元素的缓冲。通常我们只需要通过下标来访问元素，而不需要访问缓冲
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import numpy as np a=np.array([1,2,3]) b=np.array([(1,2,3),(4,5,6)]) print('a = ',a) print("a's ndim {}".format(a.ndim)) print("a's shape {}".format(a.shape)) print("a's size {}".format(a.size)) print("a's dtype {}".format(a.dtype)) print("a's itemsize {}".format(a.itemsize)) print(' ') print('b = ',b) print("b's ndim {}".format(b.ndim)) print("b's shape {}".format(b.shape)) print("b's size {}".format(b.size)) print("b's dtype {}".format(b.dtype)) print("b's itemsize {}".format(b.itemsize)) # a = [1 2 3] # a's ndim 1 # a's shape (3,) # a's size 3 # a's dtype int32 # a's itemsize 4 # # b = [[1 2 3] # [4 5 6]] # b's ndim 2 # b's shape (2, 3) # b's size 6 # b's dtype int32 # b's itemsize 4 |
二.特定array的创建
zeros：用来创建元素全部是0的数组ones：用来创建元素全部是1的数组empty：用来创建未初始化的数据，因此是内容是不确定的arange：通过指定范围和步长来创建数组linespace：通过指定范围和元素数量来创建数组random：用来生成随机数
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import numpy as np a = np.zeros((2,3)) print('np.zeros((2,3)= \n{}'.format(a)) b = np.ones((2,3)) print('np.ones((2,3))= \n{}'.format(b)) c = np.empty((2,3)) print('np.empty((2,3))= \n{}'.format(c)) d = np.arange(1, 2, 0.3) print('np.arange(1, 2, 0.3)= \n{}'.format(d)) e = np.linspace(1, 2, 7) print('np.linspace(1, 2, 7)= \n{}'.format(e)) f = np.random.random((2,3)) print('np.random.random((2,3))= \n{}'.format(f)) # np.zeros((2,3)= # [[0. 0. 0.] # [0. 0. 0.]] # np.ones((2,3))= # [[1. 1. 1.] # [1. 1. 1.]] # np.empty((2,3))= # [[1. 1. 1.] # [1. 1. 1.]] # np.arange(1, 2, 0.3)= # [1. 1.3 1.6 1.9] # np.linspace(1, 2, 7)= # [1. 1.16666667 1.33333333 1.5 1.66666667 1.83333333 # 2. ] # np.random.random((2,3))= # [[0.66485083 0.65070901 0.13474133] # [0.60233868 0.09831888 0.46165572]] |
三.Shape与操作
reshape：根据已有数组和指定的shape，生成一个新的数组vstack：用来将多个数组在垂直（v代表vertical）方向拼接（数组的维度必须匹配）hstack：用来将多个数组在水平（h代表horizontal）方向拼接（数组的维度必须匹配）hsplit：用来将数组在水平方向拆分vsplit：用来将数组在垂直方向拆分
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import numpy as np zero_line = np.zeros((1,3)) one_column = np.ones((3,1)) print("zero_line = \n{}\n".format(zero_line)) print("one_column = \n{}\n".format(one_column)) a = np.array([(1,2,3), (4,5,6)]) b = np.arange(11, 20) print("a = \n{}\n".format(a)) print("b = \n{}\n".format(b)) # zero_line = # [[0. 0. 0.]] # one_column = # [[1.] # [1.] # [1.]] # a = # [[1 2 3] # [4 5 6]] # b = # [11 12 13 14 15 16 17 18 19] b = b.reshape(3, -1)#一个3行3列的矩阵,第二参数设为-1，表示根据实际情况自动决定 print("b.reshape(3, -1) = \n{}\n".format(b)) # b.reshape(3, -1) = # [[11 12 13] # [14 15 16] # [17 18 19]] c = np.vstack((a, b, zero_line))#三个数组在垂直方向拼接 print("c = np.vstack((a,b, zero_line)) = \n{}\n".format(c)) # c = np.vstack((a,b, zero_line)) = # [[ 1. 2. 3.] # [ 4. 5. 6.] # [11. 12. 13.] # [14. 15. 16.] # [17. 18. 19.] # [ 0. 0. 0.]] a = a.reshape(3, 2) print("a.reshape(3, 2) = \n{}\n".format(a)) d = np.hstack((a, b, one_column))#hstack进行水平方向的拼接 print("d = np.hstack((a,b, one_column)) = \n{}\n".format(d)) # a.reshape(3, 2) = # [[1 2] # [3 4] # [5 6]] # d = np.hstack((a,b, one_column)) = # [[ 1. 2. 11. 12. 13. 1.] # [ 3. 4. 14. 15. 16. 1.] # [ 5. 6. 17. 18. 19. 1.]] e = np.hsplit(d, 3) # Split a into 3 将数组d在水平方向拆分成3个数组 print("e = np.hsplit(d, 3) = \n{}\n".format(e)) # e = np.hsplit(d, 3) = # [array([[1., 2.], # [3., 4.], # [5., 6.]]), array([[11., 12.], # [14., 15.], # [17., 18.]]), array([[13., 1.], # [16., 1.], # [19., 1.]])] f = np.hsplit(d, (1, 3)) # # Split a after the 1st and the 3rd column #将数组d从第1列和第3列两个地方进行拆分 print("f = np.hsplit(d, (1, 3)) = \n{}\n".format(f)) # f = np.hsplit(d, (1, 3)) = # [array([[1.], # [3.], # [5.]]), array([[ 2., 11.], # [ 4., 14.], # [ 6., 17.]]), array([[12., 13., 1.], # [15., 16., 1.], # [18., 19., 1.]])] g = np.vsplit(d, 3)#将数组d在垂直方向进行拆分 print("np.hsplit(d, 2) = \n{}\n".format(g)) # np.hsplit(d, 2) = # [array([[ 1., 2., 11., 12., 13., 1.]]), array([[ 3., 4., 14., 15., 16., 1.]]), array([[ 5., 6., 17., 18., 19., 1.]])] |
四.索引
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import numpy as np base_data = np.arange(100, 200) print("base_data\n={}\n".format(base_data)) print("base_data[10] = {}\n".format(base_data[10])) # base_data # =[100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 # 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 # 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 # 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 # 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 # 190 191 192 193 194 195 196 197 198 199] # # base_data[10] = 110 every_five = np.arange(0, 100, 5)#创建一个包含了若干个下标的数组来获取目标数组中的元素 print("base_data[every_five] = \n{}\n".format(base_data[every_five])) # base_data[every_five] = # [100 105 110 115 120 125 130 135 140 145 150 155 160 165 170 175 180 185 # 190 195] a = np.array([(1,2), (10,20)])#多维 print("a = \n{}\n".format(a)) print("base_data[a] = \n{}\n".format(base_data[a])) # a = # [[1 2] # [10 20]] # base_data[a] = # [[101 102] # [110 120]] base_data2 = base_data.reshape(10, -1)#一个10X10的二维数组 print("base_data2 = np.reshape(base_data, (10, -1)) = \n{}\n".format(base_data2)) # base_data2 = np.reshape(base_data, (10, -1)) = # [[100 101 102 103 104 105 106 107 108 109] # [110 111 112 113 114 115 116 117 118 119] # [120 121 122 123 124 125 126 127 128 129] # [130 131 132 133 134 135 136 137 138 139] # [140 141 142 143 144 145 146 147 148 149] # [150 151 152 153 154 155 156 157 158 159] # [160 161 162 163 164 165 166 167 168 169] # [170 171 172 173 174 175 176 177 178 179] # [180 181 182 183 184 185 186 187 188 189] # [190 191 192 193 194 195 196 197 198 199]] #假设我们只指定了一个下标，则访问的结果仍然是一个数组。 print("base_data2[2] = \n{}\n".format(base_data2[2])) #假设我们指定了两个下标，则访问得到的是其中的元素 print("base_data2[2, 3] = \n{}\n".format(base_data2[2, 3])) #我们也可以通过”-1”来指定“最后一个”的元素 print("base_data2[-1, -1] = \n{}\n".format(base_data2[-1, -1])) # base_data2[2] = # [120 121 122 123 124 125 126 127 128 129] # base_data2[2, 3] = # 123 # base_data2[-1, -1] = # 199 #通过”:“的形式来指定范围 print("base_data2[2, :] = \n{}\n".format(base_data2[2, :]))#获取下标为2的行的所有元素 print("base_data2[:, 3] = \n{}\n".format(base_data2[:, 3]))#获取下标为3的列的所有元素 print("base_data2[2:5, 2:4] = \n{}\n".format(base_data2[2:5, 2:4]))#获取下标为[2,5)行，下标为[2,4)列的所有元素 # base_data2[2, :] = # [120 121 122 123 124 125 126 127 128 129] # base_data2[:, 3] = # [103 113 123 133 143 153 163 173 183 193] # base_data2[2:5, 2:4] = # [[122 123] # [132 133] # [142 143]] |
五.数学运算
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import numpy as np base_data = (np.random.random((5, 5)) - 0.5) * 100 print("base_data = \n{}\n".format(base_data)) print("np.amin(base_data) = {}".format(np.amin(base_data))) print("np.amax(base_data) = {}".format(np.amax(base_data))) print("np.average(base_data) = {}".format(np.average(base_data))) print("np.sum(base_data) = {}".format(np.sum(base_data))) print("np.sin(base_data) = \n{}".format(np.sin(base_data))) # base_data = # [[-35.66944285 4.56307029 7.27959772 26.49144495 6.73664639] # [ 0.89383741 15.6060851 -5.31012 -2.88094505 39.85015085] # [-14.30186595 -4.73183095 -43.68073461 27.57136779 -13.59920839] # [ -1.21448696 -26.7323584 -10.57146785 24.08097968 6.38022583] # [ 9.60947876 44.01200782 -30.0014446 -19.35327035 -32.10892146]] # # np.amin(base_data) = -43.68073460775658 # np.amax(base_data) = 44.012007822691956 # np.average(base_data) = -1.0832481944987336 # np.sum(base_data) = -27.08120486246834 # np.sin(base_data) = # [[ 0.8965524 -0.98887266 0.83952719 0.97759255 0.43807945] # [ 0.77948133 0.10170203 0.82661465 -0.25770633 0.83632963] # [-0.98646775 0.99981101 0.2970126 0.64648344 -0.85875647] # [-0.93719056 -0.99958471 0.9114068 -0.86829838 0.09688829] # [-0.18365243 0.0297063 0.98780776 -0.48268194 -0.63884415]] |
六.矩阵
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import numpy as np base_data = np.floor((np.random.random((5, 5)) - 0.5) * 100) print("base_data = \n{}\n".format(base_data)) print("base_data.T = \n{}\n".format(base_data.T)) print("base_data.transpose() = \n{}\n".format(base_data.transpose())) matrix_one = np.ones((5, 5)) print("matrix_one = \n{}\n".format(matrix_one)) minus_one = np.dot(matrix_one, -1)#乘法 print("minus_one = \n{}\n".format(minus_one)) print("np.dot(base_data, minus_one) = \n{}\n".format(np.dot(base_data, minus_one)))#乘法 # base_data = # [[ -1. -32. -2. 6. -41.] # [-29. 16. 13. 19. -5.] # [-25. -10. -18. -41. 48.] # [-48. -3. -31. 46. 44.] # [-14. -37. 30. 27. 17.]] # base_data.T = # [[ -1. -29. -25. -48. -14.] # [-32. 16. -10. -3. -37.] # [ -2. 13. -18. -31. 30.] # [ 6. 19. -41. 46. 27.] # [-41. -5. 48. 44. 17.]] # base_data.transpose() = # [[ -1. -29. -25. -48. -14.] # [-32. 16. -10. -3. -37.] # [ -2. 13. -18. -31. 30.] # [ 6. 19. -41. 46. 27.] # [-41. -5. 48. 44. 17.]] # matrix_one = # [[1. 1. 1. 1. 1.] # [1. 1. 1. 1. 1.] # [1. 1. 1. 1. 1.] # [1. 1. 1. 1. 1.] # [1. 1. 1. 1. 1.]] # minus_one = # [[-1. -1. -1. -1. -1.] # [-1. -1. -1. -1. -1.] # [-1. -1. -1. -1. -1.] # [-1. -1. -1. -1. -1.] # [-1. -1. -1. -1. -1.]] # np.dot(base_data, minus_one) = # [[ 70. 70. 70. 70. 70.] # [-14. -14. -14. -14. -14.] # [ 46. 46. 46. 46. 46.] # [ -8. -8. -8. -8. -8.] # [-23. -23. -23. -23. -23.]] |
七.随机数
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import numpy as np #生成20个随机数，它们每一个都是[0.0, 1.0)之间 print("random: {}\n".format(np.random.random(20))); #根据指定的shape生成随机数 print("rand: {}\n".format(np.random.rand(3, 4))); #生成指定范围内（[0, 100)）的指定数量（20）的随机整数 print("randint: {}\n".format(np.random.randint(0, 100, 20))); #对已有的数据（[0, 1, 2, ..., 19]）的顺序随机打乱顺序 print("permutation: {}\n".format(np.random.permutation(np.arange(20)))); # random: [0.95075516 0.87381298 0.44899112 0.59610248 0.96922852 0.79234569 # 0.15103866 0.04605819 0.81143152 0.10017509 0.09332543 0.2620653 # 0.46254723 0.57771349 0.30671189 0.6956188 0.47801939 0.83740912 # 0.95743037 0.84286029] # # rand: [[0.49654786 0.68419816 0.23360294 0.97797805] # [0.63537403 0.71530047 0.40076013 0.10561536] # [0.42796733 0.10005927 0.41320619 0.90237485]] # # randint: [49 11 50 16 86 24 17 37 61 36 10 73 75 0 27 60 38 88 28 66] # # permutation: [ 4 16 19 5 14 10 0 18 17 13 9 1 15 11 2 12 7 3 8 6] |