字符串的拼接
1.“+”,如果是字符和数字相连,要使用str()函数对于数字进行字符转化;
2.join()
3.",",链接的两个字符串之间用空格做关联
4.占位符
tmp +=1
#print 'r'%tmp
print("row num is: %s"%(tmp))
print('value is: %s; RowNum is: %s'%(cellValue,rowNo)) #多个占位
 
python的全局变量
第一种是def consts()中进行定义(函数名自定义),在使用时记得首先要consts()一下。
def consts():
global rowNum
consts()
print(str(rowNum))
 
乱码问题
在windows下面,Python输出到控制台,打出来的汉字是乱码解决方案:
1. 直接汉字处理,直接在前面添加“u"即可:
print (u'哈哈你好')
2. 如果汉字付给了变量:
string='哈哈你好'  
print string.decode('UTF-8') 
 
def test():
global rowNum
rowNum=rowNum+1
print(rowNum)
这里注意在常量函数中声明全局变量的时候,要使用global,如果在另外一个函数里面要使用,而且需要修改,也要声明global,告知编译器我修改的是全局变量,否则会报错;但是在主函数中使用则不需要。
第二种是定义一个外部文件,再import 进来

python异常处理

import traceback
try:
....
except Exception, e:
print 'traceback.print_exc():'; traceback.print_exc()
Python文件处理
file = open(“D:\Workspace\Operation_Five\sourceCode\src\test\data\StatsGroupInfo\subscriber”)
python的文件的读入报错:
IOError: [Errno 22] invalid mode ('r') or filename: 'F:\\Dropbox\\python\test.txt'
是因为这里有\t这样的转义字符;所以需要进行处理,在前面添加r,并使用单引号:
file = open(r'D:\Workspace\Operation_Five\sourceCode\src\test\data\StatsGroupInfo\subscriber')
 
关于“//”
实操机器学习的时候,发现了下面的写法:
train_data["AgeBucket"] = train_data["Age"] // 15 * 15
train_data[["AgeBucket", "Survived"]].groupby(['AgeBucket']).mean()
默然一看// 15*15还以为是注解;其实不然,是python里面特有的写法,代表除以15之后,向下取整,这种算法也称之为地板除。
 

zip
a = np.array(['a', 'b', 'c', 'd'])
b = np.array([2,4,6,8,10])
for i1, i2 in zip(a, b):
print("{}, {}".format(i1, i2))

zip用于将数组对象打包,可以用于遍历等操作。

numpy库中的random

首先要明白randomd对象生成的数据永远都是小于的,如果想要大于1自己处理去。比如astype(np.int32),将小数进行四舍五入取整数;比如可以*10,放大十倍等
一般的玩法是:
1 import numpy.random as rnd
2 rnd.rand(row_num, col_num) #随机生成row_now行,col_num列的二维数组(矩阵)
3 rnd.random(num) #随机生成num个数(小于1的)
4 rnd.randn(row_num, col_num) #数据满足正态分布,随机生成row_now行,col_num列的二维数组(矩阵)

 python参数中的*和**

def method(* args, ** kwargs)
args的格式是元组:method("first parameter", "second parameter"...)
kwargs的格式是字典的:method(firstParam="first parametger", secondParameter="second paramerter“ ...)

 数据转化为float

从文件中获取数据,如果转成numpy的array默认是String类型,如果转化为float?

 datas=array(line.split())
datas=datas.astype(np.float)

注意这里astype并不会改变当前数组,而是改变返回值的数据类型;所以需要有一个接收值。

nonzero

在python的numpy里面这个函数的意义是返回参数数组中不为0的元素的索引(indics)。
from numpy import array
1 from numpy import nonzero
2 x=array([[1,0,0], [0,2,0], [1,1,0]])
3 print(x)
4 nonzero(x)

output:

[[1 0 0] [0 2 0] [1 1 0]] (array([0, 1, 2, 2], dtype=int64), array([0, 1, 0, 1], dtype=int64))
这个是要把两行捏在一起来看:
[0,1,2,2]
[0,1,0,1]
代表作为二维数组[0,0],[1,1],[2,0],[2,1]四个位置的元素不为0,也就说返回的一个数组是行信息,第二个数组对应的列信息,组合在一起就是一个(x,y)坐标。
 argsort
numpy里面的函数,用于对于数据进行排序。argsort里面有一个参数axis,axis=0代表沿着纵轴进行排序,axis=1代表沿着横轴进行排序。
 ar = array([[1,2],[4,3],[2,3],[0,1]])
br = ar.argsort(0)
br

输出的为:

 
array([[3, 3],
[0, 0],
[2, 1],
[1, 2]], dtype=int64)

这里大家注意了,ar的形式如下:

0)  1  2

1)  4  3

2)  2  3

3)  0  1

axis=0就是代表沿着纵向进行排序(从小到大),3)排名第一,0)排名第二,2)排名第三...,所以我们看到返回值,第一列是3,0,2,1;第二个维度(特征)继续排序,但是第二个维度排序,第二个维度值是2,3,3,1,下面对于这四个数进行排序,最小的索引是3),第二个小的索引0)一次类推;分别对于两个维度的特征进行了排序。所以可以理解axis=0就是基于特征的排序。

那么axis=1呢?那就是横向排序,即按照行进行排序:

 ar = array([[1,2],[4,3],[2,3],[0,1]])
br = ar.argsort(1)
br

返回值:

array([[0, 1],
[1, 0],
[0, 1],
[0, 1]], dtype=int64)

按照行来进行排序,或者说只是行内进行排序,上面的argsort(0)则是列内进行排序,可以这么理解,argsort(1)是样本内部各个特征值的排序,argsort(0)则是样本间的特定特征值的排序。

 python plot中的数组

如果是plot直接扔进去一个数组将会发生什么?
import numpy
arr = numpy.array([[1,2,3],[7,8,9]])
print(arr)
from matplotlib import pyplot as plt
plt.figure(figsize=(12, 4))
plt.plot(arr)
plt plt.show()

输出如下:

[[1 2 3]
[7 8 9]]
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agIFq51LX3ZEZecxkCFMh8PEiABEiABEignxOoa61DYH4gVketxlseb2GE0whNEA9zGobX3F/D8sjl8M31RXVzdT8npePti9FGSRIQ7gi4TAaW/59REMt/y99FbOu8xsZNOXSk2uOlKJB7jIoXkgAJkAAJkIB9EKhqroJPjg+WRizFpJOTNCEsgniE8wi84/EO1kStQXBBMOpb6+3jhm3hLmTXV3Z/ZRf4wBuA7Ap3tVyT3WLZNZbd4/IMuzPlsMb0UCBbY1YYEwmQAAmQAAmYkUBZYxk8L3piQdgCvHj8RUO5xOi9o/HBmQ+wKXYTwovC0dTeZMZVOdVlCUhdsNQHS52w1AsvusEoiKWeWOqKpb5Y6ow5dCdAgaw7ci5IAiRAAiRAAn1LoKC+ACcyT2BuyFw8c/QZgyAeu28spp2dhu0J2xFTGgM5fMehEwHpHCEdJKSThHSUWHCdURBvvKez84R0oJBOFBwWJ0CBbPEUMAASIAESIAES6D0BMeXIrsnG4bTD+CHoBzx++HGDIL7vwH2Y4TsDe5L2ILE8Ee2XeHir96RNfGdLXWePYek1vONxZcqheg9rJRO/7+xJ7PlDZ4/iBh50NJGsLpdTIOuCmYuQAAmQAAmQgHkIXPr7JaRVpeFAygF8E/ANHnZ52CCIxaBDjDr2X9iP1MpUmnKYB3nPZhH3OXGhEzc6caX7RbnTiSAWtzpxrRP3ujQvoKm6Z/PxKosSoEC2KH4uTgIkQAIkQAKXJyC7vkkVSdou8Oe+n+P+g/cbBPGjro/i+6Dv4Zrmios1F2nKoefDVF8KJLkBHt8Cm8YZyyXmXwvsehrwXQhk+ilTjgY9o+JaZiJgMYGcmpqK4cOHG17/7//9P6xZs+ayt2VqsGZixGlIgARIgARIQDcCbR1tiC2N1eqEp3lPwz377zEI4qePPo2fz/2M4xnHkV+XT0GsW1bUQjX5QNwh4MTnwPrRRkG88C+A0wtAwHIgJwRoa9YzKq7VRwRM1ZxX9UUcHR0duO6665CTc/mTmqYG2xexck4SIAESIAESMCeB5vZmRBRFYHPsZkw9MxV3773bIIhfOPYC5ofOx+mLp1HSUGLOZTnX5QiIKUdFJhDtDLh9AqwZYhTEi28E9r8GnFsL5EcpU442srRDAqZqzj4RyF5eXrjvvvu6xWtqsN1OyAtIgARIgARIQGcCDW0NOFdwDmuj1+Ld0+9qvYelB/HQPUO1nsTSm9g7xxuVzaqmlUMfAiKIS1OAyO3A4SnAyjuMgnjZLcCht4GwLUBRvDLl6NAnJq6CmsY2VDdaptOKqZqzTwTylClTsGGDaozdzTA12O7m49dJgARIgARIoK8J1LTUwC/XDysiV+B199cx3Gm4Jojlz7dOvYVV51dpLna1rbV9HQrn7yIgIrcoTonezcDBtwARwV2mHCKOD3+gxPIOoCyVphw6PjXl9S04nVAEhxNJeGptEG7+4RQ2+StjFAsMUzWn2QVya2srrr76apSU/PtfHTk6OkKClNeAAcpVhoMESIAESIAErJhAeVM5vLK9sCh8EV4+8bK2MyyCeJTzKLzn+R7Wx6xHSGEIGttUX1wOfQhIGUReJBC8Btg3CZAyiS5BvGYocGw6ELMXqMyiINYnI9oqRTVNOB5bgB/dEjBhpT9u+v6U9rpjzmm8tT0Ma73TkVxomR8cLS6Qjx8/jscfV/0BezBMDbYHU/ISEiABEiABErgiAsUNxXDPcscvob/gObfnDPXDY/aNwYdeH2Jr3FacLz6Plo6WK1qHbzaBgByUyz6nDs4tUwfoJgILrzcK4g13Aye/AOJdOg/ecehCQPp151Y0wuV8Hr5xjcMDy/wMgnjw3DN4b5eqw/fPRFROFVrbL+kS0+UWMVVzmn0H+fXXX8euXbt6BMLUYHs0KS8iARIgARIggR4SkA/5vNo8uKW7YXbwbDx55EmDIL53/7341OdT7EzcifiyeLRd4uGtHmK98sta6lVLNV/VWm0BsPMpYP41RlOOzeqMk8cs1ZLtGCCt2Th0ISDfKxmlddgXnoPPD8TgnkU+BkE8fJ4XPnI6j+1BWUgsqEHHJVUDbmXDVM1pVoHc2NiIP/3pT6ipqekRFlOD7dGkvIgESIAESIAE/gMB+ZDPrM6ES6oLZgXMwgSXCQZBPP7geMz0m4m9yXtxoeKC+pDn4S3dHiQx20g7A3jNAbZN6DTjkJKJX/6oTDoe7jTrSD0NiHkHhy4EROQmFdZg17mL+GRvFEbNP2sQxHcv9MZn+6PhHJqN1OI6XLJCQfzPkEzVnGYVyKZmzNRgTZ2f15MACZAACfRvAiJyUypTNNH7pd+XeODgAwZB/IjLI5pIPpRySBPN4mjHoRMBsWNOPgGc/l7ZNN/fadcsglh2inc+CfjMAzKUrbPYO3PoQqCt4xJi86qxNSATH+yOxFCHMwZBfN8SX3x1KBYHI3JxsbzBJvt1m6o5KZB1eey4CAmQAAmQgB4EpAxCyiF2Je7SyiPG7R9nEMRSPiFlFFJOkVuba5Mf8now7JM1aouAhMOA+5fAxrHG+uEF1wF7ngP8lwIXg5QpR1OfLM9J/5VAS3sHIi5WYoNvOt7ZEY67fvY0COKHV/jj+yPxOBqdj/wq+zh8SoHM7wISIAESIIF+Q0AOysmBOTk4Jwfo5CCddJiQlxywcwhx0A7cFdUrgcahDwHpQVyVDcTuV90kPgXWDjcK4kU3AHtfAYJWqS4UEUC7ZXri6gPCulZpbG3HuYxyrPJKxWtbQzHwp9MGQfzE6kDMOZYI9/hClNbap3MgBbJ1PY+MhgRIgARIwIwEpJVaaGGo1lpNWqxJq7UuQSwt2KQV25nsM5DWbBw6ERBBXJ4OnFcH9I98CKwaZBTES28CDrwJhG4ECmOUS127TkFxmdrmNvillGLJ6RS8uOkcbvvRQxPEt6hexM+tD8Z892ScSSpGVUP/+CGFApnfEyRAAiRAAnZDQMw2xHRjVdQqzYRjhFOnS90wp2GaSYeYdYhph5h3cOhE4JKq1S5OBMIdAZd3geW3GQXx8v8DXN8DIrYBJcnKpY513TplBZVK6HomFmPeyWQ8uz5IE8IiiEUYv6QE8lLPFPillkKEc38cFMj9Meu8ZxIgARKwEwJVzVWaLbPYM4tNc5cph9g3i42z2DkHFwSjvlW1AePQh4Ds+hZEASHrgf2vA0uUyVeXKcfqwcDRj4GoPWoXWTmkyW4yhy4EpBTiZFwhfjqWgMdXBxjKJW5XpROvO4Zi1dk0hKiSiqZWdmORhFAg6/JYchESIAESIAFzEChtLMXpi6exIGwBXjj2gqFcYvTe0fjgzAfYHLsZEUURaGrn4S1z8O7RHO3KACU3DAhcATi/BCz6q1EQrxsJHP8MiDsIVOf2aDpeZB4CeZWNOBKVj+8Ox+Oh5UZTDjlcJ4fsNvplIDK7EnL4juNfCVAg86kgARIgARKwSgLSg7igvgDHM47j53M/45mjzxgE8dh9YzHNexq2J2xHbGks2sRKmEMfAq2qS0FWAOC3GNj9LLDgz0ZBvPEe4NTXQOIRQDpRcOhCQL5XssrqcUC1VftStVeTNmtdts3Sfm3qnkg4BmYiTrVla1ft2Ti6J0CB3D0jXkECJEACJKADAfmQv1hzEYfTDuP7oO/x2OHHDIL4vgP3YYbvDOxJ2oOk8iS0X+LhLR1S0rlEcy2QfhbwdgB2PK5MOa7+1ZTjD8DWBwDPH4EL7kBDhW4h9feFxGgjpbgWTsp441NlwCFGHF2CWAw6pu+Lwm5l2JFcWGsTphzWmE8KZGvMCmMiARIggX5AQIw20qrSsP/Cfnzt/zUeOvSQQRDLf8vfydfkGppy6PhAiPtcyqlON7qtDyp3OiWEpYZY3Oq2PwacndspmJt50FGvrMiub0J+jWbN/KGyaBar5i5BLBbOXxyMwf7wXGXtXM9+3WZKCgWymUByGhIgARIggcsTkF1f2f2VXWDZDZZd4a6Wa7Jb/EPQD9rusewiy24yh04E6kpUScRRVRrxDbBpnLFcYv61wK5nVCnFIlVS4Q+0NugUEJdpbb+EqJxKbPLPwOSdERg81+hS96CqJ/7WNQ6u5/Mgdcb8Xumb54UCuW+4clYSIAES6PcEWjtaEVMao9UJTzs7DVI33CWIpZ5Y6opPZJ7Q6oz5Ia/j41Kdpw7NHQJOzADWjzIK4oV/UYfsXlSH7ZYDOaHKlEMdvuPQhUBzWwdCMyuwxjsNb24Lwx1zjKYcj64KwGy3BByPLUBRDQ+f6pIQtQgFsl6kuQ4JkAAJ2DkB6RwhHSQ2xW7SOkpIZ4kuQfzi8Re1zhPSgUI6UXDoREB24isygWgnwG0asHqIURAvubGzDdu5dUC+asvGg446JQWob2lHQFoZlp9JwSubQ/B/sztNOW5WvYifXhsEhxNJqkdxEcrr+UOKbkn5p4UokC1FnuuSAAmQgI0TkN7C0mNYeg2/4/EOpPewCGLpRSw9iaU3sU+ODyqbVU0rhz4ExGij9EKn8Ybr+8CK242CeNmtwKF3gLAtyrgjQZlysL2XPkkBahrbcDa5BAtPJWPihmDc+qtLnfw5ceM5LPa4AJ8LJdp1HNZBgALZOvLAKEiABEjA6gmI+5xvri+WRy7XXOnEnU4EsbjVveXxluZeJy524mbHoRMBEbmFscqaeRNw8C1g6c1GQbzyTmXlPFVZOu8EylJpyqFTSmSZsroWeCQUYe7xRDy5JlDbGZYd4oGzT2PSllCsOJOKoPQyNKidZA7rJECBbJ15YVQkQAIkYHEC5U3lOJN9BgvDFuKlEy8ZyiVGOY/Ce57vYUPMBoQWhqKxTfXF5dCHgJRB5EUCwauBfa8Ci/9mFMRrhwHHpgMx+4DKixTE+mREW6WwugnHYgrww9EEPLLS39Bh4s45nnh7ezjW+aQjLKsCUmvMYRsEKJBtI0+MkgRIgAT6nEBxQzFOZp6EQ4gDnnN7ziCIx+wbg4+8PsLWuK2IKolCSwfrIvs8GV0LtDUD2cFAwDJgz/PAwuuNgnjDGODkTCDeVf0Ov0C3kPr7QnKgNKeiAS6RefjaJQ7jlxlNOYaobhPv74rAZv9MROdWQbpRcNgmAYsK5Orqarzyyiu44447cOeddyI0VJ2avcwwNVjbTAmjJgESIIG+JyAf8rm1uXBLd8Ps4Nl48siTBkE8bv84fObzGXYl7kJ8WTzaLrEusu8z8usKLfVAhg/gMx/Y+RQw/5pfBfHvgc33A6e/A5KPq1NeZbqF1N8Xku+V9JI67A3LwYwDMRi7yGjKMUL1I/7Y+Tx2BF9EYkENOpSBB4d9EDBVc15lztuePHkytm/frk3Z2toKEcwUyOYkzLlIgARIoJOAfMhnVGXgUMohzAqYhQkuEwyC+IGDD+BLvy+xN3kvUipT1Ic8fw2s23PTpD73Uj0Br5+AbY8oU44//upSp/6U/5e/l683VekWUn9fSERuUmENdirRO805CiOVM12XKccY5Vj3mXKuc1ZiOU2JZnG047BPAhYTyLW1tbj55ptN6n1parD2mTLeFQmQAAl0T0BE7oWKC3BOdsZMv5kQEdzVck3E8azAWXBJdUFmdaZJ/w53vzKvuCyBhvLOHWDZCd6idoQdft8piGWneOeTnTvHsoMsO8kcuhBoUy51MaocYmtAJqbsjsQQB6Mpx/1LffGVSywOReYiu7yB3yu6ZMQ6FjFVc5ptBzk2NhZjxozBe++9hxEjRmDq1KloaPhXlx5HR0etWbO8BgwYYB3UGAUJkAAJWBkBKYOIK4vDzsSdmO49Hffuv9cgiKV8QsoopJwirzaPH/J65q62EEg43FkrLDXDIoblteC6zppi/6WdNcZtNIDQKy1yUC5cHZhbrw7OvbMjHHf97GnYIX5khb86aBcPt5h8FKiDdxz9l4DFBPL58+fxX//1XwgPD9fof/HFF5gzZ85lM2FqsP03rbxzEiABeycgB+UiiyOxJW4LPvT6EHKQrmuH+Pljz+OX0F/gnuUOOXjHoRMBMeWQ7hHSRUK6SUhXiS5BLN0mpOuEdJ/Ii1Auda06BcVlGlvbEZxejpVeqZi0NRQDfzK61EkLtp9VK7ZT8UUorVMHIjlI4FcCpmpOs+0gFxcX46abbjIkIigoCM88ozzfLzNMDZZZJgESIAF7ISCt1EIKQ7A+Zj0mn56Mkc4jDaYcL594GYvCF8Er2wvSmo1DJwIiiMvSOvsMS7/hVXcZBfFS9fkmfYmlP7H0KWZdt05JUQ09mtrgm1KCxacv4AVlwnHbr6Yct6hexM+tD8YC92R4JRWjqoE/pOiWFBtcyFTNaTaBLKzGjx+P1FTVvFwNBwcHfPvttxTINvgQMWQSIAHzExCzjYC8AKw6vwpvnXpLM+OQHeLhTsPxhvsbWBG5An65fhDzDg6dCIhLnTjQhW/tdKQTZ7quHeIVAzud68TBriRZCWK299IpK6hUQldsmX85mYRn1gUZTDnEvvllZeO81DMF/qmlqGtmNxa9cmIP61hUIEsdsgQwdOhQvPDCC6iquvwpXVODtYcE8R5IgAT6BwGxY/bO8caSiCV49eSrml2z5lKn7JvfPf0u1kWvw7mCc2ho+9ezGv2DkAXuskO5nOVHAefWAftfB5bcaBTEq4cAbtOAaCegIpOmHDqmp6S2GSfiCvHTsQQ8tirAUD98uyqdeMMxDKvPpiEkoxxNrezGomNa7G4pUzWnWXeQTaVparCmzs/rSYAESEAvAiUNJfDI8sD80PmYeGyioX747r13Y+qZqdgcu1mrMW5uZ12kXjlBuzJAyVH9+ANXAM4vAov+ahTE60cBJ2YAcYeA6lzdQurvC0l7wrzKRhyOysesw3F4aLmfQRAPUofr3t0ZgY1+GTifXYmWdgri/v68mPP+TdWcFMjmpM+5SIAE+gUB+ZDPr8vHsYxjmHNuDp4++rRBEN+z/x584v0JtidsR2xpLNrESphDHwKtyiI7yx/wWwTsflZ1lvizURBvuhc49Q2QeBSo40FHfRLS2a87s6weByJyMfNgDMYt9jEI4mG/eGHqnvPYFpiF+PxqtKv2bBwk0FcEKJD7iiznJQES6LcE5EM+qyYLrmmu+C7wOzzq+qhBEN9/8H587vs5nJKckFSRhPZL6tf4HPoQaK4F0s8CZ+cC2x8D5v3pV1OOPwBbHwQ8fwRSTgGNlfrEw1U0o42U4lrsCcnGp/uiMXqB0aVu9IKz2t/J1y4U1dKUg8+LrgQokHXFzcVIgATskcClv19CamUq9l3Yh6/8v8KDhx40COKHDj2EbwK+wYGUA0ivSodcy6ETARG6F9w7he/WB5RLnRLCcqhu3tXAjscBb4dOwSzCmUMXArLrK7u/24OytN1g2RXucqm7V+0Wy66x7B7LLrL8oMlBApYiQIFsKfJclwRIwGYJyK5vYnki9iTtwQyfGRh3YJxBED92+DH8EPQDjqQdQXZNNj/k9cyylEIkHlGlEV8DUiJhMOVQpRNSQiGlFFkBgJRWcOhCQOqCpT5Y6oQnq3rhwXONLnUPqnpiqSuW+mKpM6Yg1iUlXKSHBCiQewiKl5EACfRfAq0drYguica2+G2YdnYaxu4baxDEz7o9i7khc3Ei8wQK65VrGod+BOSwXNxB4PhnwLqRRkEsh+ucX+o8bCeH7uTwHYcuBKRzREhmudZJQjpKSGeJrh1i6Tgx2y1B60BRXMPDp7okhIv0mgAFcq/R8Y0kQAL2SqCpvQnhReHYGLsRU85Mwei9ow2C+MXjL2JB2AJ4XvREaWOpvSKwvvuSX7dLO7WoPcDRj4HVg42CWNqvSRu2kPVAgWrLJu3ZOHQhIL2FpcfwMtVrWHoOS+9hEcQ3K1MO6UksvYmlR3FFPX9I0SUhXMRsBCiQzYaSE5EACdgqgfrWegTlB2FN1Bq84/GO1ntYehAPcxqGSScnYWnEUvjk+KCq+fK92m31/q0ybjHaEMMNMd5wfQ8QI46ukonltwEu73YadhQn0pRDxwRWN7ZqLnTiRvf8hmCIO50I4luVW5241i32uKC52ImbHQcJ2DIBCmRbzh5jJwES6BWB6uZq+Ob6YlnkMrzm/pomhDVTDuVW97bH21gdtRqB+YGoa63r1fx8Uy8IiBVzYYyyZt7YadG89GajIBYL5yMfKkvnXZ3Wzjy81QvAvXtLaV0zTsUX4efjiXhyTaChXGKgKp2YtDUUK71SEZRehoYW7tr3jjDfZa0EKJCtNTOMiwRIwGwEypvK4ZntiYVhCyElEiKG5TXKeRTe93wfG2I2IKwoDI1tPLxlNujdTdTeCuRFAEGrgL2vAIv/ZhTEa4cDxz4FYvcDVdkUxN2xNOPXC6ub4BaTjx+OxuORlf4GQXznHE+8vT0c633SEZ5VgeY2mnKYETunskICFMhWmBSGRAIkcGUEiuqLcDLzJBxCHPCc23MGQTxm3xh8fPZjOMY7IqokCnL4jkMnAm1NwMUgwH8psOd5ZcpxnVEQbxgDuH8JJBwGannQUaeMaF0jsssb4BKZh69cYnH/Ul+DIB7icAZTdkdiS0AmYnKrlIEN2xPqlReuYx0EKJCtIw+MggRIoJcE5EM+pzYHR9OP4segH/HE4ScMgnjc/nH4zOcz7E7cjYSyBLRdYl1kLzGb/rYWVZ6S4QP4zAN2PgnMv+ZXQfx7YMv9wOnvgOQTQH2Z6XPzHb0iIN8raSV1cA7LwYwDMRi7yGjKMXL+WUxzjsLO4ItILKhBhzLw4CCB/kyAArk/Z5/3TgI2SECMNsRw42DKQXwb8C0ecXnEIIjFoEOMOsSwQ4w7OqSulUMfAk3qAGPqaeDMbMDxYWXK8cdfXerUn9smAF5z1Nc9gaZqfeLhKprIFbG7Q4nej53PQ0RwV8u1MQu9NZG8V4nldCWa2YOYDwwJ/CMBCmQ+ESRAAlZNQERuckUynJOd8YXvFxh/cLxBEE9wmYBZgbPgkuqCrOosfsjrmUnZ+U06BnjMAjarHWEHtTMsXSZkp3jnU4DvAiDTF2ip1zOqfr2WlEFEq3IIKYt4f1cEhvzGlEPKJ752idPKKXIqGvi90q+fFN58TwhQIPeEEq8hARLQjYCUQcSWxmJHwg5M956Oe/ffaxDETx15Cj8F/wS3dDfk1eXxQ163rKiFagqAeFfg5BfAhruN9cMLrwecJgIBy4DsYKCNBhB6pUUOyoWpA3Pr1ME5OUAnB+m6dojlgJ0ctDsWU4ACdfCOgwRIwDQCFMim8eLVJEACZibQ3N6MyOJIbI7bjKleUyEH6bq6TDx/7HnMC52HU1mnUNygbIQ59CEgbdQqs4CYvaqbxHRgzVCjIJZuE/smAcFrVBeKSOVSx4OO+iQFWis1aakmrdUmbQnFwNlGlzppwTZXtWKTlmxldTTl0CsnXMd+CVhUIN90000YMmQIhg8fjp4E0pNr7DdVvDMSsA8C0kotpCAE66LXYfLpyRjpPFITxEP3DMUrJ17B4vDFOJtzFhVNFfZxw7ZwFyKIy1JVn+GdwOEPgJV3GgWx9COWvsRhm4GiOGXKwbpuvVIqZhs+F0o08w0x4bhNmXHIDrGYc4hJx8JTyTibXAIx7+AgARIwLwFTNedV5lxeBHJ5eXmPpzQ12B5PzAtJgAT6jEBNSw388/yx8vxKvHnqTQx3Gq4JYvnzDfc3tL+Xr8t1HDoREJFbFK9E7xbg0DvAsluNgnjF7UokTwEitwOlF+hSp1NKZBmxYxZbZocTSXh6bZBm1yyCWOybX1E2zmLnHJBWBrF35iABEuhbAqZqTgrkvs0HZycBmycgO7+yAyw7wbIjLDvDIohlp1h2jGXnWHaQG9oabP5ebeYGOpSgyo8Czq0F9r8GLLnRKIjXDAHcpgHRzkqhZdKUQ8ekFtc043hsAWa7JeDRVQGG+uE75pzGm9vCsMY7DSGZ5TTl0DEnXIoEughYVCDffPPNGDlyJEaNGgVHR8dus2JqsN1OyAtIgASumEBJQ4lWIyy1wlIz3FU/fPfeu7WaYqktlhpjqTXm0ImAHJTLCQECl6sDdC8AC/9iFMTrRwMnPgfiDgHVeToFxGWkjVpeZSMOR+XjW9c4PLjczyCIB6tuE5N3RmCTfwaicirR2k5TDj4xJGBpAqZqTrPuIBcWdjomlZaWYtiwYQgMDPwXHiKcJUh5DRgwwNK8uD4J9GsC2oe86h5xLOOY1k1Cukp0CeJ79t+DT7w/0bpPSBeKNtm15NCHQKvajc/0U63VFgK7nlGt1q41CuJN44BT36iWbG5AXYk+8XAVrcNKRmk99ofnYubBGNy72McgiIf94oUPnc5je1AW4vOr0U6XOj4xJGB1BCwqkH9Lw8HBAStWrLgsIFODtTraDIgEbIyAfMhLf2HpM/xd4HeY4DrBIIjvP3i/1pfYKclJ61NMUw4dk9us6rXTvICzPwPbHwXm/elXU44/KJOOhzrNOlJOAY2VOgbVv5e6pEw5LhTVYve5i5i+LwqjFxhNOUYv8Man+6LhFJqNlOJayLUcJEAC1k3AVM1pth3khoYG1NUpK1I15L/HjRsHT0/lsnSZYWqw1o2e0ZGA9REQlzpxoBMnOnGkE2e6rh3ih10e1pzrxMFOnOzkWg6dCDSojh4XTgKePyib5vHKpU4JYTHlmHc1sONxwPsXIN0baK7VKSAuI7u+cXnV2BaYhal7IjHU4Yxhh3ic2i3+8lAsDkTkIqusnv26+biQgA0SMFVzmk0gZ2VlaWUV8ho0aBAWLlS/GuxmmBpsd/Px6yTQ3wm0X2pHQlkCdifuxgyfGRh3YJxBED9x+An8GPQjjqQdQU5tDj/k9XxY6lTP58QjgPtXwMZ7jOUSC/4M7H4W8FsMXFQlaa2NekbVr9dqae9AZHYlNvpl4F1VLzzoZ6Mpx0OqnnjW4TgcUfXFUmfMQQIkYPsETNWcZhPIvUFnarC9WYPvIQF7JtDa0Yrokmg4xjvi47Mf/4Mpx7Nuz8IhxAEnM0+isL7zfACHTgSqcoDYA8Dxz4B1I4yCeNFfgb0vA0ErgdwwZcpBAwidMoKm1g6EZJRj1dk0vO4Yitt/MppyPL46AD8dS8CJuEKU1PLwqV454TokoCcBUzUnBbKe2eFaJHCFBMSUI6woDBtjN+J9z/cxynmUYYf4xeMvYkHYAnhme6KssewKV+Lbe0xATDnKM4Co3cDRj4DVg42CeIk6iHzgDSBkA1AQDXS093haXnhlBKS3sF9qKZaqXsMvq57D0ntYehBLL+Jn1gVh3slk1aO4WOtVzEECJGD/BCiQ7T/HvMN+RKCutQ6B+YFYHbUab3u8jRFOIzRBPMxpGCadnIRlkcvgk+uD6ubqfkTFwrd6SdVqlyQBEdsA1/eAFQONgnj5bYDLZCBctbmUa+RaDl0IVDW0wiupGPPdk/Hc+mDNnU4EsbjVvbjpHBafvgC/lFLU0pRDl3xwERKwNgIUyNaWEcZDAiYQqGqu0gTv0oilmgAWISyCWISxCGQRykH5QRDhzKETAdn1ld1f2QU+8Caw9CajIF51F3Dkw87d4/J0mnLolBJZprSuGe7xhfj5eCKeWB1oOFA3UJVOvLY1FKu8UhGcXo7GVu7a65gWLkUCVkuAAtlqU8PASOBfCUgphOdFT600QkokujpMjN47GlPOTNFKKaSkQkorOHQi0N6q6oPDO+uEpV540Q1GQSz1xMc/VfXF+wGpM5byCg5dCBRUN+FodD6+PxKPR1b4GwTxXepw3Ts7wrHBNx0RFyvpUqdLNrgICdgeAQpk28sZI+5HBOSwnByamxsyF3KIrksQj9k3Rjtkty1+m3boTg7fcehEoK2ps4OE/xJgz3PAguuMgnjj2M7OEwmHgVoedNQpI1qHlYvlDTgUmYuvVHu1+5b4GgTxENV+7YPdkdgakIlY1ZatjaYceqWF65CATROgQLbp9DF4eyIgH/LZNdlaWzVpr/b44ccNgljar0kbNmnHllieCGnPxqETgRZVnpKhegxLr+EdT3T2HpYexA6/7+xJfPp7IPmEauherlNAXEaMNlKL6+CsjDc+2x+NMQu9DYJ45PyzmOYchV3KsCOpsEYZ2HDXnk8MCZCA6QQokE1nxneQgFkIiNGGGG4cSDmAbwK+gRhxdO0Qi0GHGHWIYYcYd9CUwyzIezaJuM+leHS60Ykr3S9//NWUQ7nViWuduNelnQGaqns2H6+6YgIichMLarAj+CI+UhbNI+Z5GQTx2EXe+PxADPaG5Shr5zr2675i2pyABEhACFAg8zkgAZ0IiBVzUkWSZs38ue/nEKvmLkEsFs5i5eya5oqsmix+yOuUE22Z+lIg6Rjg8S2w+b7OnWHZIZ5/LbDracBXmRhl+gEt9XpG1a/XkjKIqJwqbPbPxPu7IjBkrtGlbvwyX3zjGgeX83nIqWjg90q/flJ48yTQdwQokPuOLWfu5wTaOtoQWxqL7Qnb8Yn3J7hn/z0GQfzUkacw59wcHMs4hry6PH7I6/ms1OQD8S7AyS+A9aON9cMLrwecXgAClgPZ54A2GkDolZbmtg6EZVVgrXc63toehjvnGF3qJqz0xw9HE3A8tgCF6uAdBwmQAAnoQYACWQ/KXKNfEGhub0ZkcSQ2x27G1DNTcffeuw2CeOKxiZgXOg8eWR4oblA2whz6EJCuEZVZQLQz4PYJsGaoURAvvhHYNwk4txbIP69MOdr0iYmroKGlHYFpZVhxJhWvbgnBwNmdLnViyvHU2iA4nEiCR0IRyupoysHHhQRIwDIEKJAtw52r2gGBhrYGnCs4h3XR6/Du6XcxwrnTlGPonqF49eSrWBKxBGdzzqKiqcIO7tZGbkEEcWkKELkDODwFWHmHURAvuwU49DYQthkoilemHB02clO2H2ZNYxt8LpRgkccFTNx4DrcqMw4RxPLnxA3B2t97J5egupHdWGw/27wDErAPAhTI9pFH3oUOBGpaauCf548VkSvwhvsbGO40XBPE8uebp97EyvMrEZAXALmOQycCInKL4jpFr4hfEcFahwn1WnF7p0gWsSyimT2IdUqK8kBRdsyn1Q6w7ATLjrDsDIsglp1i2TFefiYFAWoHuV7tJHOQAAmQgDUSoEC2xqwwJqsgUN5UDq9sLywOX4yXT7ys7QyLIB7pPBKTT0/Wdo5DCkJoyqFntqQMQsohgtd0lkdImUSXIJbyCSmjkHIKKaugINYtM8U1zVqN8I9uCZCaYRHD8rpjzmm8uS1Mqy0OzaygKYduGeFCJEACV0qAAvlKCfL9dkNAaoPds9zxS+gveP7Y84b6Yaklnuo1FVvitmg1xlJrzKETATkoJwfm5OCc00RADtJ1CWI5YCcH7eTAnRy849CFgPTrzq1ohKvqIiHdJB5Y5mcQxINVt4n3VNeJTf4ZWheK1vZLusTERUiABEjA3AQokM1NlPPZBAH5kM+rzYNbuht+Cv4JTx550iCI791/L6Z7T8eOhB2IK4tTzls8vKVbUlsbOluq+S7obLEmrda6TDmkBZu0YpOWbNKajUMXAvK9Iv2F94Xn4IuDMbhnkY9BEA9X/Yg/VH2JtwdlISG/Bu10qdMlJ1yEBEig7wlQIPc9Y65gBQTkQz6rOgsuqS6YFTgL0ne4qwfx+IPjMdNvJpyTnZFckayct3h4S7eUidmGmG6I+ca2CcqlTplxiCAWcw7HhzvNOsS0Q8w7OHQhIC51yYW1mhPdJ3ujMEo503WVTIxe4I1PlXOdk3KwEyc7uZaDBEiABOyRgMUFckdHB0aMGIFnn322W76mBtvthLzAbgmIyE2pTNGc6L70+xLiTNcliMWx7tuAb3Eo5RAyqjLoUqfnU9CgOnqILbPYM4tNc5cph9g3i42zz7xOW2exd+bQhYDs+sbmVcMxMBNT90RiqIPRlOO+Jb746lAsDkbkIqusnv26dckIFyEBErAGAqZqzqvMHfSqVavw5ptvUiCbG2w/m6/tUhviy+KxK3EXPvP5DOP2jzMI4icOP4HZwbNxNP0ocmtz+SGv57NRWwQkHAbcvwI2jjXWDy+4DtjzHOC/BLgYpEw5aAChV1pa2jsQmV2JDb7peGdHOO762WjK8fAKf3x3OB5Ho/ORX9WoV0hchwRIgASsjoBFBXJ+fj4mTJgAX19fCmSrezSsO6CWjhZElURha9xWfOT1EcbsG2MQxM+5PQeHEAeczDyJwvpC674Re4pOukZUZQOx+4HjnwLrRhgF8aIbgL0vA0GrgNxwoJ39bvVKfWNrO85llGPV2TS8tjUUA3/qNOWQ1xOrAzHnWCJOxhWitJaHT/XKCdchARKwfgIWFcivvPIKoqKi4O/vT4Fs/c+KRSNsbGtEaGEoNsRswHue72GU8yiDIH7pxEtYGLYQntmekNZsHDoREEFcng6c3wUc+RBYNcgoiJfeBBx4EwjZABTGKJc69rvVKSuobW6DX0oplpxOwUubzuG2X005blG9iJ9dH4T57sk4k1SMygb+kKJXTrgOCZCA7RGwmEB2d3fH9OnTNWKXE8iOjo6QIOU1YMAA2yPMiHtFoK61DoH5gVgVtQpvebyFEU6dLnXDnIbhNffXsDxyOXxzfVHdXN2r+fmmXhC4pFp2FScC4Y6Ay2Rg+f8ZBbH8t+t7QMQ2oCRJudSxvVcvCPfqLSJ0RfDOO5msCWARwrI7LMJYBLIIZb/UUk04c5AACZAACfSMgMUE8g8//IAbbrgBN910E6677jr87//+L95+WzlhXWaYGmzPEPAqayBQ1VwFnxwfLI1YikknJ2lCWASx2De/4/EO1kStQVB+EOpb660h3P4Rg+z6FkSrXeD1ajf4DWCJ+gG1qwex7BYf/QiI2q12kTNoyqHjEyGlEFISIaURj68OMJRLSOmElFBIKYWUVEhpBQcJkAAJkEDvCJiqOc1+SE/CZolF75Jny+8qbSzF6YunsSBsAV48/qKhXGL03tGYcmYKNsVuQnhROJraeXhLtzy3t6j64DBVJ7wScH4JWPRXoyBeN1LVFX+m6osPqDrjHN1C4kLKOFAdlpNDc3J4Tg7RddUPy+E6OWQnh+3k0J0cvuMgARIgARIwDwEKZPNw5CzdECioL8CJzBP4+dzPeOboMwZBPHbfWEw7Ow3b4rchpjQGrR2si9TtYWpVXQouBgJ+i4Hdqs3igj8bBfHGe4BTXwOJRwDpRMGhCwGtX7dqpyZt1aS9mrRZ6xLE0n5N2rBJOzZpy9ZGUw5dcsJFSIAE+icBqxDIPUVvarA9nZfXmZeAfMhfrLmIw2mH8UPQD3j88OMGQXzfgfsww3cG9iTtQWJ5Itov8dfA5qV/mdmaa4F01WPY+xfVc/hxZcqheg9rphx/ALY+AHj+AFw4CUivYg5dCIjRhhhuiPGGGHDcvdDbIIjFoEOMOnYrww4x7uigKYcuOeEiJEACJCAETNWcfVJi0dNUmBpsT+fldVdG4NLfLyGtKg37L+zH1/5f46FDDxkEsRh0fOX/lfa11MpUmnJcGWrT3i3ucymnOt3oHB/qFMIiiMWtbvujyr1urnKx8wKaa0ybl1f3moCIXLFkFmtmsWgWq+auHWKxcBYrZ7F0ziilKUevIfONJEACJGAGAqZqTgpkM0C39Slk1zepPEnbBZbdYNkV7nKpe9T1UXwf9D1c01y1XWTZTebQiUBdCZDkpkojvgE2jTOWS8y/Ftj1DOC7EMjyB1obdAqIy7S2X0JUThU2+WfgvV0RGDzX6FL3wDI/fOsaB9fzecitaOT3Ch8XEiABErAiAhTIVpQMaw2lraMNsaWx2J6wHdO8p+Ge/fcYBPHTR5/GnHNzcDzjOPLr8vkhr2cSq/OAuEPAic+B9aOMgnjhXwCnF4DA5UBOiDLlUIfvOHQh0NzWgdDMCqz1Tseb28JwxxyjKcejqwLwo1sCjscWoKiGh091SQgXIQESIIFeEqBA7iU4e36bdI6IKIrA5tjN+ODMB7h7790GQfzCsRcwP3Q+PLI8UNKgdiw59CEgO/EVmUC0E+A2DVgzxCiIF98I7H8NOLdWtTyIUqYc7HerT1KA+pZ2BKSVYfmZFLyyOQT/N9tDK5m4WfUifmptEBxOJOF0QhHK6/lDil454TokQAIkYA4CFMjmoGjjczS0NSC4IBhro9fi3dPvar2HpWRi6J6hePXkq1pvYu8cb1Q2q5pWDn0IiCAuvQBEblcGHO8DK243CuJltwKH3gHCtgBF8cqUg+299EkKUNPYBu/kEiw8lYyJG4Jx668udfLnxI3nsMjjAnwulGjXcZAACZAACdguAQpk281dryOvaamBX64fVkSuwOvurxtMOYY7Dcdbp97CqvOrNBe72lbV9YBDHwIicovigNBNwMG3gGW3GAXxyjuAwx8oS+edQFkqTTn0yYi2SlldCzzUDrDsBD+5JlDbGZYd4oGzT+PVLSFYcSYVgWoHuUHtJHOQAAmQAAnYDwEKZPvJ5X+8k/KmcpzJPoNF4Yvw8omXtZ1h2SEe6TwSk09PxvqY9QgpDEFjm+qLy6EPASmDyIsEgtcA+14FFv/NKIjXDgOOKRv2mL1A5UUKYn0yoq0itcHHYgrww9EETFhpNOWQWuK3todptcVhWRWQWmMOEiABEiAB+yVAgWyHuS1uKIZ7ljscQhzwnNtzhvrhMfvG4EOvD7ElbgvOF59HSwfrInVLf1szkB0MBCxTB+gmAguvNwriDXcDJ2cC8a7qd/j5uoXU3xeSDis5FQ1wUV0kvlHdJMYvM5pyDFHdJt5XXSc2+2dqXSikGwUHCZAACZBA/yFAgWzjuZYP+dzaXLilu2F28Gw8eeRJgyC+d/+9+NTnU+xM3Im4sji0XWJdpG7pbqkHMn1Va7UFwM6ngPnX/CqIfw9svh/wmAUkH1envMp0C6m/LyTfKxmlddgbloPPD8RA+g539SAeofoRf6T6Eu8IvojEghqacvT3h4X3TwIk0O8JUCDb2CMgH/KZ1Zk4lHIIswJmYYLLBIMgHn9wPGb6zcTe5L24UHFBfcjz18C6pbepGkj1BLzmANsmdJpxaC51f1QmHQ+rv/9Jff000FSlW0j9fSEx5UgqrMEu5UQ3zTkK4kzXJYjFse4z5VznrBzs0krqII52HCRAAiRAAiTQRYAC2cqfBRG5InZF9Ir4feDgAwZB/IjLI5pIFrEsolkc7Th0ItBQ3rkDfPo7YIvaEXZQO8MiiGWneOeTgM88IMMHaKnTKSAu09ZxCTG5VdgakIkPdkdiqIPRlOO+Jb74yiUWhyJzcbG8gf26+biQAAmQAAlclgAFspU9IFIGEV8Wr5VFSHnEuP3jDIJYyiekjELKKaSsgi51OiavthBIOAy4fwlsGGOsH15wHbDnecB/KXAxCGijAYReWZGDchEXK7HeJx3v7AjHXT97GnaIH1nhj++PxMMtJh/5VTx8qldOuA4JkAAJ2AsBCmQLZ1IOysmBua1xW7UDdHKQrsu2WQ7YyUG7k5knUVRfZOFI+9Hy0oO4Klt1kdinukl8CqwdbhTEi24A9r4CBK1SXSgilEtdaz8CY9lbbWxtR3B6OVZ5pWLS1lAM/MnoUvfE6kD8fDwR7vGFKK1VByI5SIAESIAESOAKCFAgXwG83rxVWqlJSzVprfae53sY5TzKIIhfOvGS1opNWrJJazYOnQiIIC5LU32GdwFHpgKr7jIK4qU3dfYlDt0IFMYolzr2u9UpK6htboNfSikWn76AFzedw22/mnLconoRP7c+GPPdk+GVVIyqBv6QoldOuA4JkAAJ9BcCFMh9nGkx2xDTDTHfEBOOEU6dLnXDnIZpJh3LI5drph1i3sGhE4FLqla7OBEI3wq4vAssv80oiFcM7HSui9gGlCQrlzrWdeuUFVQqoeuZWIx5J5PxzLogiBCWQ3Vi3/yysnFe6pkCv9RS1CnhzEECJEACJEACfUmAAtnMdMWOWWyZxZ5ZbJq7TDnEvllsnMXOWWyd61tVGzAOfQjIrm9BFBCyHtj/OrDkRqMgXj0EOPoxELUHqMikKYc+GdFWKVGlECfiCvHTsQQ8vjrAUD98uyqdeN0xFKvPpiEkoxxNrezGomNauBQJkAAJkIAiYDGB3NzcjDFjxmDYsGEYNGgQ5s6d221CTA222wnNcEFpYyk8sjwwP3Q+Xjj2gqFcYvTe0fjgzAfYFLsJEUURaGrn4S0z4O7ZFO3KACUnFAhcATi/BCz6q1EQrxsJnJgBxB0EqnN7Nh+vMguBvMpGHInKx6zDcXhouZ9BEA9Sh+ve3RmBjX4ZOJ9diZZ2CmKzAOckJEACJEACvSZgqua8qtcr/dMbpQNDfX3nLmpbWxvGjh2LsLCwy05varDmirVrHok5vy4fxzOOY865OXj66NMGQTx231hM856G7QnbEVMag9YO1kWam/9/nK9VdSnICgD8FgG7nwUW/NkoiDfdC5z6Gkg8CtQV6xZSf19IvleyyupxICIXXx6KhbRZ6+pBPOwXL0zdcx7bArMQl1eNdtWejYMESIAESIAErImAqZrTbAL5txAaGxsxcuRIhIeHW51Azq7JhmuaK74P+h6PHX7MIIjvO3AfZvjOwJ6kPUgqT0L7JR7e0u3Bbq4F0s8C3g7A9seUKcfVv5py/AHY+gDg+SNwwR1orNQtpP6+kBhtpBTXwkkZb3y6LxqjF3gbBPHoBWcxfV8UdivDjgtFtTTl6O8PC++fBEiABGyAgEUFckdHB4YPH47f/e53+O47Zbjwb4ajo6NWByKvAQMG6I508unJmih+8NCD+Nr/a+y/sB9pVWk05dAzEyJ0RfCK8N36oHKnU0JYTDnErU4E8llVniOCuZkHHfVKi+z6xudXY3tQlrYbLLvCXTvE9y72wcyDMdgfnqusnevZr1uvpHAdEiABEiABsxGwqEDuuovq6mo8/PDDSExUnQUuM0wN1hyUEssTcbHmIj/kzQGzp3PUlXSWREhphJRIiBiWl5ROSAmFlFJk+QOtDT2dkdddIYHW9kuIyqnU6oQnq3rhwXONLnUPqnrib13jcFjVF0udMQ1srhA2304CJEACJGBxAqZqzj4psRAKv/zyC1asUIeqrEwgWzxD/SGA6rzOQ3NyeG79KKMgXvgXdcjuxc7DdnLoTg7fcehCQDpHhGSWY413Gt5wDMMdc4ymHI+uCsBstwQcjy1AcQ1NOXRJCBchARIgARLQlYDFBHJZWRlk51hGU1MTxo8fD3d39Wt0CmRdHwDdFxNTDmmnFu3U2V5N2qx17RBL+zVpw3ZuXWdbNppy6Jae+pZ2+Ksew8tUr+FXVM9h6T0sJRM3q17ET68NgsOJJNWjuAjl9fwhRbekcCESIAESIAGLEbCYQI6Pj8eIESMwdOhQDB48GPPmzesWgqnBdjshL+h7AmK0IYYbYrwhBhwrbjcK4mW3dhp1iGFHcQJNOfo+G4YVqhtbcTa5BAtPJeP5DcEGU45blVvdCxvPYbHHBfhcKEFNE005dEwLlyIBEiABErASAqZqzj4rsegJD1OD7cmcvMbMBC6pHraFscqaeVOnRfPSm42CeOWdnVbOYuks1s6ym8yhC4Gyuhacii/C3OOJeHJNoLYzLDvEA2efxqQtoVjplYqg9DI0qJ1kDhIgARIgARLo7wRM1ZwUyP39ifnn+29X/Z7zIoDg1cC+V4HFfzMK4rXDgGOfAjH7gMqLFMQ6PjuF1U04FlOAH47G45GV/oYOE3fO8cTb28Oxzicd4VkVaG6jKYeOaeFSJEACJEACNkKAAtlGEmU1YbYpR8CLQYD/UmDP88DC642CeMMY4ORMIOEwUFNgNSHbeyDSNSK7vAEukXn42iUO9y81mnIMUd0mpuyOxJaATETnVqGNphz2/jjw/kiABEiABMxAgALZDBDteooW5XaY4QP4zAd2PgnMv+ZXQfx7YMv9wGnVvzr5OFBfZtcYrOnmRBCnl9TBOSwHMw7EYOwioynHiHle+Nj5PHYGX0RiQQ06lIEHBwmQAAmQAAmQgGkEKJBN42X/VzdVAamnAa+fgG2PqP57f/zVpU79Kf8vf5/qqVqPqOs4dCEgIlfEroheEb8j5581lEyMWeitiWQRy2lKNIujHQcJkAAJkAAJkMCVEaBAvjJ+tv9u2fmVHWCPWcBmtSPsoHaGpe2a7BTvfKpz5zjTF5CdZA5dCEgZRIwqh5CyCCmPGOJgNOWQ8omvXGK1cgopq6Aphy4p4SIkQAIkQAL9jAAFcj9LOGoLgXjXzlrhDXf/xpRD1RI7TQQClgHZwUAbDSD0ejTkoJwcmFuvDs7JATo5SNdl2ywH7OSgnVtMPgrUwTsOEiABEiABEiCBvidAgdz3jC23grRRk+4R0kXi2HRAukp0mXJItwnpOiHdJ/IilUud6kbBoQuBxtZ2raWatFabtDUUA38yutRJCzZpxSYt2Urr+EOKLgnhIiRAAiRAAiTwTwQokO3pkRBBXJaq+gzv7Ow3LH2HuwSx9COWvsTSn1j6FEu/Yg5dCIjZhm9KiWa+ISYctykzDtkhvkX1IhaTjgXuyZpph5h3cJAACZAACZAACVieAAWy5XPQ+wjEpU4c6MK2AIfeAcSZrksQrxjY6VwXuR0ovUCXut5TNvmdFcqOWWyZfzmZpNk0d5lyiH2z2DiLnbPYOtc106XOZLh8AwmQAAmQAAnoQIACWQfIZluiQwmq/Cjg3Dpg/2vAkhuNgnj1EMBtGhDtBFRk0pTDbNC7n6ikthkn4gox2y0Bj60KMNQP365KJ95wDMPqs2kIySxHUyt37bunyStIgARIgARIwPIEKJAtn4P/HEF7C5ATAgQuB5xfVKYcfzEK4vWjgBMzgLhDQHWuNd+FXcUmXSPyKhtxOCof37rG4cHlfgZBPOhnT0zeGYGNfhk4n12JlnYKYrtKPm+GBEiABEig3xCgQLamVLc2AFn+gO9CYNczqtXatUZBvGkccOobIPEoUFdiTVHbdSwiiDPL6rE/PBczD8Zg3GIfgyAe9osXpu45j+1BWYjPr0Y7Xers+lngzZEACZAACfQfAhTIlsx1cw2Q5gWcnQtsfwyY96dfTTn+AGx9EDgzG0g5BTRWWjLKfrW2GG1cKKrFnpBsTN8XhdELjKYc8t+f7ovWvpZSXEtTjn71ZPBmSYAESIAE+hMBCmQ9s91QAVxwBzx/UAL4AeVSp4SwHKqbdzWw43HA2wFI9waaa/WMql+vJbu+cXnV2BaYpe0Gy65wVw/ie9VusewaH4jI1XaRacrRrx8V3jwJkAAJkEA/IkCB3JfJritWJRFHVGnE18DGe4zlEgv+DOx+FvBbrEoqAoDWxr6MgnP/hoDUBUt9sNQJv6vqhaVuuEsQP6TqiWcdjtPqi6XOmIKYjw4JkAAJkAAJ9E8CFMjmzLsclos7CBz/DFg30iiIF/1VHbJ7CQhaCeSGKVMOdfiOQxcC0jkiJKNc6yQhHSWks0SXIJaOEz8dS9A6UEgnCg4SIAESIAESIAESEAIWE8h5eXl4+OGHceedd2LQoEFYu3ZttxkxNdhuJ7ySC8SUozwDiNoDHP0YWD3YKIiXDAAOvAGErAcKVFu2jvYrWYnvNYGA9BaWHsNLVa/hl1XPYek9LIJYehE/sy5I603smVgM6VXMQQIkQAIkQAIkQAL/joCpmvMqc2EsKipCdHS0Nl1dXR0GDhyI5OTky05varDmilWbR0w5SpKAiG3KgOM9QIw4ukw5lt8GuLwLhDsq445EmnKYFfzlJ6tqaIVXUrHmRvfc+mDNnU4EsbjVvbjpHBafvqC52ImbHQcJkAAJkAAJkAAJ9ISAqZrTbAL5n4ObOHEizp49a30COWq32g1+E1h6k1EQr7pLWTl/qCydd6ld5HSacvTkSTPTNaV1zXCPL8TPxxPx5JpAQ7nEQFU6MWlrKFZ5pSI4vRwNLdy1NxNyTkMCJEACJEAC/Y6AVQjk7Oxs3Hjjjait/dfuDY6OjlodiLwGDFClC3oPKZVYOxw49ikQux+oyqYg1jEHBdVNcIvJx/dH4vHICn+DIL5zjife2RGO9T7pCM+qQHMbTTl0TAuXIgESIAESIAG7JmBxgVxfX49Ro0bh6FFlgNHNMDXY7ubr0dfbeHirR5zMcJF0jcgub8ChyFx85RKL+5b4GgTxEIczmLI7ElsDMhGTW4U2mnKYgTinIAESIAESIAES+HcETNWcZi2xaGtrwxNPPIFVq1b1KDumBtujSXmRxQiIKUdaSR2cw3Lw2f5ojFnobRDEI+efxTTnKOwMvoikwhp0qGs5SIAESIAESIAESEAPAqZqTrMJZNktfPfddzFz5swe36epwfZ4Yl6oCwERuYkFNdihRO/HzucxYp7RlGPsIm98fiAGe5VYTleimT2IdUkJFyEBEiABEiABEvg3BEzVnGYTyMHBwbjqqqswdOhQDB8+XHt5eHhcNkmmBsuMW5aAlEFEq3KIzf6ZeH9XBIbMPWPYIR6/zBdfu8TB5XwecioaKIgtmyquTgIkQAIkQAIk8BsCpmpOswnk3mTB1GB7swbf03sCclAuTB2YW6cOzr21PQxykK7LlGPCSn/8cDQBx2IKUKgO3nGQAAmQAAmQAAmQgLUSMFVzUiBbayYtEJe0UgtMK8OKM6mYtCUUA2d3utSJKYe0YJurWrF5JBShrI6mHBZID5ckARIgARIgARLoJQEK5F6C649vE7MNnwslWORxARM3nsOtyoxDBLH8OXFDMBaeSsbZ5BJUN7b2Rzy8ZxIgARIgARIgATshQIFsJ4nsi9sQO+bTagfY4UQSnl4bpO0MiyAW++ZXlI3z8jMpCFA7yPU05egL/JyTBEiABEiABEjAQgQokC0E3hqXLa5pxvHYAvzoloBHVwUY6ofvmHMab24LwxrvNIRm0pTDGnPHmEiABEiABEiABMxHgALZfCxtaiZpo5Zb0QhX1UXiW9c4PLDMzyCIB6tuE5N3RmCTfwaicirR2n7Jpu6NwZIACZAACZAACZDAlRCgQL4Sejb0XhHEGaX12B+eiy8OxuDexT4GQTxc9SP+0Ok8tgdlISG/Bu10qbOhzDJUEiABEiABEiABcxOgQDY3USuZT1zqkgtrsfvcRUzfF4XRC84aBPHoBd74VDnXOYVmI7W4DnItBwmQAAmQAAmQAAmQQCcBCmQ7eRJk1zcurxqOgZmYuicSQx2Mphzj1G7xl4dicTAiF1ll9TTlsJOc8zZIgARIgARIgAT6hgAFct9w7fNZW9o7EJldiY1+GXhnRzgG/Ww05Xh4hT++OxyPI1H5yK9q7PNYuAAJkAAJkAAJkAAJ2BMBCmQbyWZTawfOZZRj1dk0vO4Yitt/6jTlkNfjqwMw51giTsYVoqS22UbuiGGSAAmQAAmQAAmQgHUSoEC2zrygrrkNfqmlWHI6BS9tOofbfjXluEX1In52fRDmnUyGZ2IxKhtoymGlKWRYJEACJEACJEACNkqAAtlKElelhO6ZpGLMd0/WBLAIYdkdFmH8ohLIIpT9UkpRq4QzBwmQAAmQAAmQAAmQQN8RoEDuO7aXnblUlUK4xxdqpRFPrA40lEsMVKUTr20NxSqvVK2korG13UIRclkSIAESIAESIAES6J8EKJB1yrscljsanY/vj8RDDtF11Q/fpQ7XySG7Db7piLhYCTl8x0ECJEACJEACJEACJGA5AhTIfcBeTDkuljdobdW+Uu3V7lviaxDEQ1T7tQ92R2rt2GJVW7Y2mnL0QQY4JQmQAAmQAAmQAAn0noDFBPKUKVNw7bXXYvDgwT2O3tRgezzxFV4oRhtiuOGsjDfEgOPuhd4GQTxq/ll8sjcKu5Rhhxh3dNCU4wpp8+0kQAIkQAIkQAIk0LcETNWcV5krnMDAQERHR9ukQBaRK5bMYs38kbJoHqGsmrtKJsYu8sbnB2KwLzxHWTvX0ZTDXA8M5yEBEiABEiABEiABnQhYTCDL/WVnZ9uEQG5tv4SonCps8s/Ae7siMGSu0aXugWV++MY1Di7n85Bb0UhBrNODy2VIgARIgARIgARIoK8IUCB3Q1bE7x1zjKYcE1b640e3BByPLUBhdVNf5YXzkgAJkAAJkAAJkAAJWIiA1QtkR0dHSJDyGjBggO6Y1ninweFEEjwSilBe36L7+lyQBEiABEiABEiABEhAXwJWL5B/i8PUYPVFydVIgARIgARIgARIgATsgYCpmtNsh/QEnq3UINtDonkPJEACJEACJEACJEACPSNgMYH8xhtv4Prrr8d///d/44YbbsCOHTu6jdjUYLudkBeQAAmQAAmQAAmQAAmQwD8RMFVzmnUH2dRsmBqsqfPzehIgARIgARIgARIgARIwVXNSIPOZIQESIAESIAESIAESsGsCFMh2nV7eHAmQAAmQAAmQAAmQgKkEKJBNJcbrSYAESIAESIAESIAE7JoABbJdp5c3RwIkQAIkQAIkQAIkYCoBCmRTifF6EiABEiABEiABEiABuyZgUwL5mmuuMbjqdbnr6fHnTTfdZJF19bg3rtHp0igv5tnIwp6fC+bZ/vPMHNt/jvlvdv/IsSXzLJrTlGHRLhamBGrOa039KcKca3Mu/Qgwz/qxtuRKzLMl6euzNnOsD2dLr8I8WzoD+qxvK3mmQNbneeAqFiBgK9+EFkBjV0syz3aVzn97M8yx/edY7pB5Zp6tiQAFsjVlg7GYlQD/sTUrTqudjHm22tSYLTDm2GworXoi5tmq02O24Gwlz/1SIDs6Opot0ZzIegkwz9abG3NGxjybk6Z1zsUcW2dezB0V82xuotY5n63kuV8KZOt8ZBgVCZAACZAACZAACZCANRCgQLaGLDAGEiABEiABEiABEiABqyFg1wLZ09MTt99+O2677TYsWbLkX6C3tLTgtdde074+duxYZGdnW01iGEjPCHSX41WrVuGuu+7C0KFDMWHCBOTk5PRsYl5lVQS6y3NXsIcPH8ZVV12F8+fPW1X8DKZnBHqSZxcXF+17etCgQXjzzTd7NjGvsioC3eU5NzcXDz/8MEaMGKH92+3h4WFV8TOY7glMmTIF1157LQYPHvxvL/773/+Ozz//XNNfkuPo6OjuJ9X5CrsVyB0dHbj11luRlZWF1tZWDBs2DMnJyf+Ad9OmTZg2bZr2dwcPHtTEMoftEOhJjv38/NDY2Kjd1ObNm5lj20mvIdKe5FkurqurwwMPPIB77rmHAtlO85yenq6JpqqqKu0OS0tLbfBO+3fIPfl+/uijj7R/r2XI57b0weawLQKBgYGa6P1PAll+6HnqqacgQjksLEzbpLS2YbcCOTQ0FE888YSB9+LFiyGv3w75ulwno729HVdffbWWLA7bINCTHP/2TmJiYnDffffZxs0xSgOBnuZ55syZcHd3x0MPPUSBbIPPT0/yPGvWLGzfvt0G744hdxHoSZ4//vhjLF26VHuLXD9u3DgCtEEC8lv5/ySQJccHDhww3JX8tr+oqMiq7tJuBbL8qnXq1KkG2M7Ozvjss8/+Ab4kLj8/3/B3suNcXl5uVQliMP+ZQE9y/Nt3S/4XLFhApDZGoCd5lh9+Xn75Ze3OKJBtLMG/htuTPL/wwgsQkSw/6MpvCuRX9Ry2RaAneRahNGTIENxwww34wx/+gKioKNu6SUarEbicQH722WcRHBxsICUlkNZWGme3AtnV1fVfBPKMGTP+4bGVGrZ/FsgVFRV8tG2EQE9y3HUre/fu1T5Qpe6cw7YIdJfnS5cuaaK46wwBBbJt5bcr2u7yLNfJh+qLL76ItrY2XLx4URNQ1dXVtnnD/TTqnuRZzo6sXLlSIyQ7yFJzLt/nHLZF4HIC+ZlnnvkXgWxtPwjZrUDuya9xWGJhW99s/xxtT3Is7/H29sadd97JekUbTXd3ea6pqdHKo6ROUV7/8z//g7/85S9Wtxtho/h1C7u7PEsgcmZk9+7d/7DrFBkZqVuMXOjKCfQkz7J5lZeXZ1jslltu4b/fV45e9xlYYqE78p4tKDXF8k0luwxdh/SSkpL+4c0bN278h0N6kyZN6tnkvMoqCPQkx/KrdymdkcM9HLZJoCd5/u2dcQfZfvMsJRWTJ0/WblDK4f72t7+Bv/WzrXz35PtZDm91/SB04cIF7Qdeng+yrTxLtJcTyKdOnfqHQ3pjxoyxuhu02x1kIS2nJAcOHKgJpIULF2rwf/75Z5w4cUL77+bmZrz66qtamxFJjnS84LAtAt3l+NFHH8Wf//xnDB8+XHs9//zztnWDjFYj0F2eKZDt40HpLs8ikr766ivtV+5Soyrdhzhsj0B3eZbOFVJnLt2n5N9tLy8v27vJfh7xG2+8geuvvx7//d//rZVC7dixA1u2bNFeMuR7+dNPP9X0mXwvW1v9scRo1wK5nz+fvH0SIAESIAESIAESIIFeEKBA7gU0voUESIAESIAESIAESMB+CVAg229ueWckQAIkQAIkQAIkQAK9IECB3AtofAsJkAAJkAAJkAAJkID9EqBAtt/c8s5IgARIgARIgARIgAR6QYACuRfQ+BYSIAESIAESIAESIAH7JUCBbL+55Z2RAAmQAAmQAAmQAAn0ggAFci+g8S0kQAIkQAIkQAIkQAL2S4AC2X5zyzsjARIgARIgARIgARLoBQEK5F5A41tIgARIgARIgARIgATsl8D/D/qXPmExAVXiAAAAAElFTkSuQmCC" alt="" />
此时走的plot函数就是如下红框范围的api
如果plot直接收了一个数组(正常情况应该是上图1的api参数),那么plot函数会认为传入的是y值的数据,x值将会被处理为样本的索引;那么代码中数据中第一个数据点[1,2,3]将会被处理为:[0,1],[0,2],[0,3],第二个数据将会被处理为[1,7],[1,8,],[1,0],然后纵向进行点组合[0,1]和[1,7]做线性组合,[0,2]和[1,8]做线性组合,以此类推。

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" 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