python时间-time模块
time是python自带的模块,用于处理时间问题,提供了一系列的操作时间的函数。
以下说明针对于 python2.7,其他版本可能有所差异。
模块提供了两个种表示时间的格式:
1.时间戳,是以秒表示从“新纪元”到现在的时间,称为 UTC 或者 GMT。这个“新纪元”指的就是1970年1月1日。所以时间戳指的就是从“新纪元”到某一个时间一共过去了多少秒,可能是一个整数,也可能是一个浮点数。至于为什么会这样,有兴趣的可以读下这篇文章:戳这里
2.一个包括 9 个元素的元祖,这 9 个元素分别为:
year:4位数,表示年,例如:2016
month:表示月份,范围是 1-12
day:表示天,范围是 1-31
hours:小时,范围是 0-23
minute:分钟,范围是 0-59
seconds:秒,范围是 0-59
weekday:星期,范围是 0-6,星期一是0,以此类推
Julian day:是一年中的第几天,范围是 1-366
DST:一个标志,决定是否使用夏令时(关于夏令时:戳这里),为 0 时表示不使用,为 1 时表示使用,为 -1 时,mktime() 方法会根据 date 和 time 来推测。一般情况下用不着。
FUNCTIONS
1. asctime([tuple]) -> string
将元祖格式的时间转换成字符串格式。
例如: time.asctime((2016,5,11,12,30,50,5,163,0))
aaarticlea/png;base64,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" alt="" />
如果 tuple 没有给,将调用 localtime() 方法,获取现在的时间。
2. clock() -> floating point number
这个有点特殊,会因系统的不同而不同,在 win 平台中,第一次调用,返回的是进程运行的实际时间。而第二次之后的调用时是第一次调用以后到这次调用时间。(实际上是以WIN32上QueryPerformanceCounter()为基础,它比毫秒表示更为精确)
代码示例:
import time if __name__ == '__main__':
time.sleep(1)
print "clock1:%s" % time.clock()
time.sleep(1)
print "clock2:%s" % time.clock()
time.sleep(1)
print "clock3:%s" % time.clock()
输出:
aaarticlea/png;base64,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" alt="" />
所谓的进程运行的时间,我觉得更像运行第一次调用的这段代码所需的时间,因为不管在第一次调用前用 sleep 停顿又或是进行一其他操作,其输出变化也不大。
import time if __name__ == '__main__':
b = []
for x in range(1000):
b.append(x)
time.sleep(1)
print "clock1:%s" % time.clock()
time.sleep(1)
print "clock2:%s" % time.clock()
time.sleep(1)
print "clock3:%s" % time.clock()
aaarticlea/png;base64,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" alt="" />
我也不知道是不是我理解错误还是其他,现在先这样理解着,有问题我以后再改正。
而在 Unix 系统中(虽然 win 也是由 unix 发展而来的),它返回的是“进程时间”,它是用秒表示的浮点数(时间戳)。
同样的代码,在CentOS6.7中运行:
aaarticlea/png;base64,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" alt="" />
其输出为:
aaarticlea/png;base64,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" alt="" />
下面是一段实验代码:
import time,urllib if __name__ == '__main__':
print 'start at:',time.ctime() try:
ur_open = urllib.urlopen('http://www.facebook.com')
except:
print 'error',time.ctime()
time.sleep(1)
print "clock1:%s" % time.clock() try:
ur_open = urllib.urlopen('http://www.facebook.com')
except:
print 'error',time.ctime()
time.sleep(1)
print "clock2:%s" % time.clock() try:
ur_open = urllib.urlopen('http://www.facebook.com')
except:
print 'error',time.ctime()
time.sleep(1)
print "clock3:%s" % time.clock()
我试图多次打开一个“不存在”的网站,看看所谓的“进程时间”是什么:
aaarticlea/png;base64,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" alt="" />
可以看到我时间花费了1分多种,但是进程时间到最后却只有 0.03 秒,参照这篇文章(戳这里),我觉得应该是进程占用 cpu 的时间,因为打开远程网页属于远程 I/O 操作,并不需要大量的 cpu 计算,所以进程时间就很短了。当然这是我的推测,暂时没有找到相应的文章说明,就先这样理解着吧,以后有错再改。
3. ctime(seconds) -> string
将一个时间戳(默认为当前时间)转换成一个时间字符串。相当于 asctime(localtime(seconds)) 。
aaarticlea/png;base64,iVBORw0KGgoAAAANSUhEUgAAAM4AAAAeCAIAAADrQ7a8AAACEklEQVR4nO1a0Y7DMAjLX9/n7146KUuAmECSdrN1OrUVwRk4wKqV1xNQyukdEGHcOoelXH/EF4BpJDaBUguhvEtuYe0d4SNAf1K88Idu7qPpibM3Hqg2G210Vqjt6ZICPVNqNkLCAtX2lVIT3VJtBuTQZKmtfKJ/PrQUjRG61eyUmhdqaBJrm5YAsQHV/xsbO5Ga5SJ2Ss2LtVXt4oglW6s3cyxZ7JSaFydnNW9dSWHJYqfUvNj6DdQuDHgLGw5P2qoUdtsDoWHTe7XhGF5fNPZ9/9KaqdZnc9lrP8Yt0YDRCWGi0f8sGCBiEyg1YhOaaYO/2CEupIuhn6BV1kTu2mHy51H2H1mOe5hmFwPiipJopq01suzdOY62qok7wLldm0uvoFpYcaFEpBZhTzwhtVI1A0N84vIUIO/6Pe6OSk3zGaxqLrHOse+U2vB2EcDX/fLD6Wrfu+1diRfTWwWhHXcwNyC7Vvyajzk3Y0xLbfWkjvruozC8Rnxqt73sQOd7pJbO7i1LLj/9cnGkcxFNwOd4OHWukJrLea7UvAkIsgedIPH0WiZiXmqRwyca31Nqri52sKrZbfoZUnNFIdLshk3zSAN1eTglNe/y+0pNPNbaicenS/Eg1iNq7RwpLfY+kf2IluCcgLMHlxtrxVyAW8ITN4eVvjXK9RMocUOckdrqA0TcEAsTrv3Mi/hNUGrEJvwDq+1002Hd3lAAAAAASUVORK5CYII=" alt="" />
4. gmtime([seconds]) -> (tm_year, tm_mon, tm_mday, tm_hour, tm_min, tm_sec, tm_wday, tm_yday, tm_isdst)
将一个时间戳格式的转换为UTC时区(0时区,中国为 UTC+8)的元祖格式。如果没有给参数,则默认为本地时间。
aaarticlea/png;base64,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" alt="" />
然而我实际的时间是14点,14 = 6 + 8。所以要注意下时区。
5. localtime([seconds]) -> (tm_year,tm_mon,tm_mday,tm_hour,tm_min, tm_sec,tm_wday,tm_yday,tm_isdst)
将一个时间戳转换为当前时区的元祖格式。如果没有给参数,则默认为本地时间。
aaarticlea/png;base64,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" alt="" />
6. mktime(tuple) -> floating point number
将一个元祖格式的时间转换为时间戳格式。
aaarticlea/png;base64,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" alt="" />
7. sleep(seconds)
线程将推迟指定的时间后运行,单位为秒。其精度为亚秒级。
关于精度级别:
分钟级:以分钟为单位,即速度按分钟计算,7200转/分
秒级:以秒为单位,即速度按秒计算,1GHz/秒
亚秒级:没有达到秒的速度,即1GHz/1.2秒
8. time() -> floating point number
返回当前时间的时间戳。
如果系统的时钟支持,可能会出现分数的形式。
9. strftime(format[, tuple]) -> string
把一个代表时间的元组转换为指定格式的字符串,如果没有传入 tuple ,将调用 localtime() 。如果元组中任何一个元素越界(不在范围内),将抛出 ValueError 错误。
关于format的表格:
格式 | 含义 | 备注 |
%a | 本地(locale)简化星期名称 | |
%A | 本地完整星期名称 | |
%b | 本地简化月份名称 | |
%B | 本地完整月份名称 | |
%c | 本地相应的日期和时间表示 | |
%d | 一个月中的第几天(01 - 31) | |
%H | 一天中的第几个小时(24小时制,00 - 23) | |
%I | 第几个小时(12小时制,01 - 12) | |
%j | 一年中的第几天(001 - 366) | |
%m | 月份(01 - 12) | |
%M | 分钟数(00 - 59) | |
%p | 本地am或者pm的相应符 | 1 |
%S | 秒(01 - 61) | 2 |
%U | 一年中的星期数。(00 - 53星期天是一个星期的开始。)第一个星期天之前的所有天数都放在第0周。 | 3 |
%w | 一个星期中的第几天(0 - 6,0是星期天) | 3 |
%W | 和%U基本相同,不同的是%W以星期一为一个星期的开始。 | |
%x | 本地相应日期 | |
%X | 本地相应时间 | |
%y | 去掉世纪的年份(00 - 99) | |
%Y | 完整的年份 | |
%Z | 时区的名字(如果不存在为空字符) | |
%% | ‘%’字符 |
备注:
1.“%p”只有与“%I”配合使用才有效果。
2.文档中强调确实是0 - 61,而不是59,闰年秒占两秒。
3.当使用strptime()函数时,只有当在这年中的周数和天数被确定的时候%U和%W才会被计算。
参考资料:戳这里
例子:
aaarticlea/png;base64,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" alt="" />
10. strptime(string, format) -> struct_time
将字符串格式的时间转换成元祖格式的。是上面方法的逆向。
aaarticlea/png;base64,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" alt="" />
总结:
aaarticlea/png;base64,iVBORw0KGgoAAAANSUhEUgAAArsAAAH+CAIAAAATfFGXAAAgAElEQVR4nO2d/7/UxL3/+y/d96EVvwEqXxWqdK/4FX2ghVuLrUgE7bkilcKxCmIJSsXrlfuRWiy0dTlgQYoVhVIoXFlbaL2AFSvrQ4QqnKvUVrz5/JDd7GQyyU6SyXuy2dfrp3NyXmfy2vdOZp6ZZLNfGSlYzWaz6F0YFzLzCJl5hMw8QmYeITOPlJm/4nme53kjIyOetlKZm81mQS0jsyhk5jEjM48ZmXnMyMxjrkxmEINCyMxjRmYeMzLzmJGZx4zMPGYQg66QmceMzDxmZOYxIzOPGZl5zCAGXSEzjxmZeczIzGNGZh4zMvOYQQy6QmYeMzLzmJGZx4zMPGZk5jGDGHSFzDxmZOYxIzOPGZl5zMjMYwYx6AqZeczIzGNGZh4zMvOYkZnHDGLQFTLzmJGZx4zMPGZk5jEjM48ZxKArZOYxIzOPGZl5zMjMY0ZmHjOIQVfIzGNGZh4zMvOYkZnHjMw8ZhCDrpCZx4zMPGZk5jEjM48ZmXnMIAZdITOPGZl5zMjMY0ZmHjMy85hBDLpCZh4zMvOYkZnHjMw8ZmTmMYMYdIXMPGZk5jEjM48ZmXnMyMxjVhNDs9lsNpsjIyNNbaUyp1JxMZCZJwYy88RAZp4YyMwTA5l5YuTPjDUGhZCZx4zMPGZk5jEjM48ZmXnMyswgBoWQmceMzDxmZOYxIzOPGZl5zCAGXSEzjxmZeczIzGNGZh4zMvOYQQy6QmYeMzLzmJGZx4zMPGZk5jGDGHSFzDxmZOYxIzOPGZl5zMjMYwYx6AqZeczIzGNGZh4zMvOYkZnHDGLQFTLzmJGZx4zMPGZk5jEjM48ZxKArZOYxIzOPGZl5zMjMY0ZmHjOIQVfIzGNGZh4zMvOYkZnHjMw8ZhCDrpCZx4zMPGZk5jEjM48ZmXnMIAZdITOPGZnjzIODgwSVQAsWLChb3zBrRmYec2UygxgUQmYeMzLHmW1PlFBHZesbZs3IzGOuTObqEANOyxI0ODhoqs4GzZU5iozH8N81/f+FilDadwH9mceMzDzmihODvem4N2SqzlH1LatJHJahdHFmv339/4WKUNp3oTKzAn8MZOaJYYAYRgpWs9ksehe+MMjGya9M0ZXvTxVaUtsdp9+V9i1mG+sMCpl5VJnMVVtj0P/3/lG0Mmbf7v6svM6rzlzn/ixp2ZT2XajMeSR/DGTmiZE/M4ih+gIxFCEQQ+UFYmCLgcw8MUAMHWGQjROIoQiBGCovEANbDGTmiQFi6AiDbJxADEUIxFB5gRjYYiAzTwwQQ0cYZOMEYihCvUUMdYdqbsNgg57nNdya+UbTR3DqcX/JGw7EwBYDmXligBg66s95S0cghiLU78TQcGuWccFLIgbPwGsGMbDFQGaeGCCGjtjmLYOnVn5TrkPxw54B9SgxZBzw4/+t7hA59bqhavc5MdSdQvusphKJITfUgBjYYiAzTwwQQ0dFEkMIEowRQ2dASx748grE0NreKrGZapeaGBpuzW+iXYpQVYK/hthJsdWvVOcPnSbCwFB3Is2p99FxKtOEd1BzG4qGhTbIcYW3MvKacyMDiIEtBjLzxAAxdMRGDKYUGnSLPGUDMchzh4lql5gYhGWUhuvWW5sEOhXZyd+csDUop+ARyykeHHW3W2udxty65PSDtww+FgR7C0yCpRXPab9A+TV7eZdWQAxsMZCZJwaIoaPCiKFzSuOPVcII6Z+DhU6EFCdRYhNxZ2lFIkOPEINcubjTYmkOSPi3utPeLJ9sGqh2yYlBmiiDqoQvyrSXW9RbI6Qc2MT6hSf9kC3UmmppJ3KJKCanuFkM1GlT8Zq9vIsMIAa2GMjMEwPE0BHbGoNEDKETIdWpkDiWdcZBeSwr8MJEDxCD6vRTfVocqmfSv4WmEJkQDFS7xMQgAFb7RYZm4rBqbiNma6RMwe9R3JWgTb2PyOwd2SYySXhRKDmQ6jV7IIbuQmYec2Uygxh0lEQMMfc3iGOcYuiLnuUWt8hQfmJQTuAxp5te/PzR+WM9fN4bnTjyV7vUxNBSh5riixkYFfWQCxy0opyJA7DT30fiGkMMMSgXPVSvOdJKWoEY2GIgM08MEENHtoghtDobIoZgLVaS6iytv4lBObDHrUR3ahv3b/KZZp8RQ8N1IjNmp1ShGbXh1hSL+sFWcQ0tsljW2Ry+UTK5tY435j6GziKdCrRDfuFeB9Vrxp2PGkJmHnNlMoMYdJSTGFSjFq5KCFKelHZfY0j4N+mydp9dlYjeNxOagFW31Si3SvfpSO7I5QDpzgPFf6k+diEyteLOhchvYsv1zvsYGz9zFUEMfDGQmSeGAWIYKViV+LbrHMQQOYtSnKUpfjUpvzKlrrzq9DPpPgbhPvy4f5PucSjozsfyltSEkqbcUjzBKVH5Lkl4+LbrsgqZeYRvu86h1hmM8rMSLUsMMXhxn00PzVq5R7cElX+NwfMUp5+xn5WIrBbE/ZuwqB75dGXuauu86sx17gFiKOgzx6ZkIhzWGNhiIDNPjPyZQQz2hCc4caoDaH3wBCdDKrZfll4gBrYYyMwTA8TQUUkG2VRq4CnRjKr38VOioQwCMbDFQGaeGCCGjjDIxgnEUIRADJUXiIEtBjLzxAAxdIRBNk4ghiIEYqi8QAxsMZCZJwaIoSMMsnECMRQhEEPlBWJgi4HMPDFADB1hkI0TDzH0p3KWLs7sN/40ZFU6b7GoyswK/DGQmScGiKEje7NGb8hUnaMaHBy0/eLsaHBwMGfp4sy2XxnUkf47WJlZgT8GMvPEADF01Lfzlo6kua3MPTJVy/6ryxmjhP159+7dyae/ruvqnysvX75c35yq5QTzwoULH3nkkcwtW8kc1Y4dO8rWN8yakZnHXJnM1SEGg2bMZDxm1JnHbCXzPffcs2nTpswto848ZmTmMVcmM4hBIcxkPGbUmcfMn/m9994jou9973uZW0adeczIzGOuTGYQg0KYyXjMqDOPmT/zbbfdRkSXXHJJ5pZRZx4zMvOYK5MZxKAQZjIeM+rMY+bPfMUVV/hvzfHjx7O1jDrzmJGZx1yZzCAGhTCT8ZhRZx4zc+bt27cHt9yKtzKUObMRMzLzmJGZx1zxb7s2qPyZ/eHSRBZdoc486s86p9K3vvWtgBgWLFiQrRHUmUfIzKPKZMYag0I49+Uxo848Zs7M77zzjvix3okTJ2ZrGXXmMSMzj7kymUEMCmEm4zGjzjxmzsw/+tGPpGeBBLcylDazKTMy85iRmccMYtAVZjIeM+rMY+bMPH78eIkYNm7cmKFl1JnHjMw85spkBjEohJmMx4w685jZMr/88ssUUfBUhnJmNmhGZh4zMvOYQQy6wkzGY0adecxsme+6664oMUyYMCFDy6gzjxmZecyVyQxiUAgzGY8ZdeYx82Q+evQoEQ0MDEShwb+VoYSZzZqRmceMzDxmEIOuMJPxmFFnHjNP5pUrV0ZZQbyVoYSZzZqRmceMzDxmEIOuMJPxmFFnHjNP5uA5j1E98MAD5cxs1ozMPGZk5jGDGHSFmYzHjDrzmBkyb9myZcGCBd/61rduuummSZMmScQwadKkEmY2bkZmHjMy85hBDLrCTMZjRp15zMyZd+7cSUTz5s3729/+duLEibfeemvPnj3Hjx8vc2YjZmTmMSMzjxnEoCvMZDxm1JnHzJx53bp1RPTEE0/kaRl15jEjM4+5MplBDAphJuMxo848ZubMDz/8MBH9/Oc/z9My6sxjRmYec2UygxgUwkzGY0adeczMme+8804i+v3vf5+nZdSZx4zMPObKZAYxKISZLLO58ipJncvcNyZMmEBEH374YZ6WUWceMzLzmCuT+SvNZrPZbI6MjDS1lcqcSsXFYM7sE0POGD1UZ/1e2OuyW+ds5lTKGeN3v/sdEU2YMCFny6nUh3U2ZU4lZM5sTqUyZ8Yag0L5M/ffGkPdIace/NZwa+3fwn8IthHV3EbdkT+5X3MbqXZUd1r/0nBrigcBdJoL/k1pDFpsuLVIBOHF2K9zFjNn5k2bNhHR4OBgzpZRZx4zMvOYK5MZxKAQiCGLWZzKY4ihPVu3J+W6E56e27+HSELijQiBBNAgKzT7i8Qg2SOwE2qv4dbEP9uvc3ozZ+bvfe97RLRp06acLaPOPGZk5jFXJjOIQSEQQzZz3am5DfXJvnC6L0zZCcTQmqSjKxTCFtWCgKDWn8MLGTXHSSYGL8QgEi+Uo85pzZyZp0yZQkTvvPNOzpZRZx4zMvOYK5MZxKAQiCGzuSX1GoMEE0497qpEmBjcuAsOATHIlBJsFTGl61WJONYJtVqSOpezb7zzzjtENHny5Pwto848ZmTmMVcmM4hBIRBDWnNr6hcn6Nj7GGKvIoQsqdYYQosNqq1tNHHqXa9KBArdvhCoP/uzpjnhJoa0LaPOPGZk5jFXJjOIQSEQQxazdEofRwx1h6hWq/lzt+JM3qlH72MQ24ghhqQ1hoZbcxyn1VCtBmIoKsbg4CDF3MSQtmXUmceMzDzmymQGMSgEYshi9ifo6IWG8NJ/3anValSribcHRE77o2sMylUHvTWGIFfyVYlu1yRaJGK/zunNbJknT55MMTcxpG0ZdeYxIzOPuTKZQQwKgRiymKU1BuGCQIgNHLd9Q6LK7Un/k8QOmsRQd1rLFnqflei0ovysp/06pzfzZG40GkQ0ZcoUIy2jzjxmZOYxVyYziEEhEEMWc2cqDn3AIHJFoeNTrUfEryhE20tx52NKYmi9AsVf7Nc5vZkn82OPPUZEK1euNNIy6sxjRmYec2UygxgUAjFkMbfuaJSfZxAiBje4SiD+p2qNQWKI0N+S1xjUzaYgBvEV4NOV2mb/4dCHDx820jLqzGNGZh5zZTKDGBQCMaQ21x0ix3Ei87tCesRg7nkM0r8lPvPR/2N0j51t/dmfu5pff/11IrrxxhtNtYw685iRmcdcmcwgBoVADGnNwU0JCTc+tifd9kQeZ419gGNk0SGGGDoNC3/tusbQfnJ17Cv0d92f/bmr+cEHHySi//zP/zTVMurMY0ZmHnNlMoMYFAIxZDZXXiWpc6n6xueff37RRRcR0V//+ldTLaPOPGZk5jFXJjOIQSEQQ2Zz5VWSOpeqb2zZsoWI5syZY7Bl1JnHjMw85spk/spIwWo2m0XvwrjyZ/aJwUQWXfVPnflrK6p/6qyvu+66i4jWr19vsE3UmUfIzKPKZMYag0JYY+AxZ8scV9vMMVDnPDGOHz/uvyPnzp0z2DLqzGNGZh5zZTKDGBQCMfCYQQw85kIzL1u2jIiWLVtmtmXUmceMzDzmymQGMSgEYuAxgxh4zMVlDhYYjh8/brZl1JnHjMw85spkBjEoBGLgMWtmPnDgwNKlSxcvXrx06dKlS5f6tV0q6MCBA2XLXHSMkmT+4Q9/SET//u//brxl1JnHjMw85spkBjEoBGLgMetnnjRpUtxTHiZNmpQzBuqcreWzZ8/6H6r84x//aLZlD3XmMiMzj7kymUEMCoEYeMz6mYeGhuKIYWhoKGcM1Dlby2vXriWie++913jLHurMZUZmHnNlMoMYFAIx8Jj1M+/duzeOGPbu3ZszBuqcoeUvvvjiiiuuIKJ9+/aZbdkX6sxjRmYec2UygxgUAjHwmFNlHjduXBQXxo0blz8G6pyh5fXr1xPRN7/5TeMt+0KdeczIzGOuTGYQg0IgBh5zqsyLFi2KEsOSJUvyx0Cd07b8ySefjBkzhoh+9atfmW05EOrMY0ZmHnNlMoMYFAIx8JhTZd6+fXuUGF5//fX8MVDntC37t5Xce++9PZSZwYzMPGZk5jGDGHQFYuAxp8182WWXibhw+eWXG4mBOqdq+eDBg379jx071iuZeczIzGNGZh4ziEFXIAYec9rM0oWJRYsWGYmBOqdq+c477ySiJ598Mm0M1JknBjLzxOjPzCAGhUAMPOa0mXfu3CkSw86dO43EQJ31W964cSMRTZs27csvv0wbA3XmiYHMPDH6MzOIQSEQA485beYLFy6MHj3ar+3o0aMvXLhgJAbqrNny2bNn/U+sbNu2LUMM1JknBjLzxOjPzPi2a4XyZ/ZnNRNZdNUndXYcx6+t4zhFROqqPqmzUkuWLCGiu+++20hryernOnMKmXlUmcxYY1AIaww85gyZt23b5tc2OM3NHwN11ml5165dfuXffvvtbDFQZ54YyMwToz8zgxgUAjHwmDNkPn/+/MDAwMDAwPnz503FQJ27tnzmzBn/qz2eeeaZzDFQZ54YyMwToz8zgxgUAjHwmLNlnj9//vz58w3GQJ27tjx//nwiuueee/LEQJ15YiAzT4z+zAxiUAjEwGPOlnl4eHh4eNhgDNQ5ueXnnnuOiMaPH3/q1Kk8MVBnnhjIzBOjPzODGBQCMfgaHByMPmYRitPg4GCed7CcfWP//v3+q3v11VdzxrDen3XM6PN9qODIrV5/zmMGMegKxODL7mHci8rzDkKibI0btnsQZEfGO5KkysyDIAaFQAy+4l4FFJWyViCGzLJLDPqtQb0uEEOcQAy6AjH4wuipLxPEUHdqbkPc0HBr0pbY/2ydKmnY6w45dfE3/38abk1x6lVzG53GqVYLPE7dz1t3a8qd1p24MA235tQ7OdqvMRwLxAAxCcQQJxCDrkAMvjB66qsQYvAabi00j8ZN7GGPQBCKP0pTswANsqTJvO7UHKdWcxvtSb+dt80A4s7bG6KvoN1u3SHHDZBIagPEADEJxBAnEIOuQAy+MHrqyyQxhOb78Pm+F1p4CE3H4sTfoYIIH4hbuqxhdP5cd2puo+7U3EbDrTn1gBiSUUXJNArk8ZsNu0EMEI9ADHECMegKxOALo6e+chKDONO2zuKFuVxeBQgBQXsZQHSEicGNu+AQ7EWexoOt7UbrDtVqrZUBctwOMbTbVqCDFDn0q79DESNaP7f3CWKAeARiiBOIQVcgBl8YPfWVkxg8z5NnVWHNv7M59hxePD9Pv8YQ2oe8tbWSIUzyDbfWvqVBSKy+YKIgEf+yResbQpy659Xdmnx1AsQA8QjEECcQg65ADL4weurLHDHET7ztKwOJTbSJIYQQIjfEEINiZhc5wt9xmD/as7t0r0Ld6YAFOXWJWYK/+tvbHNJ6YcEuQQwQj0AMcQIx6ArE4Aujp77MEEP7pDv4NbrYr0sM0hqDctVBb43BUy5t+LN9CyPEGyuEz1f4+2k03MgHJyJ3W/r00dkjiAHiEYghTvi2a13h2659YfTUV8533O2s8cdfeOhM+XFLEO00Kj6IskOKqxLUQYfwakLn4w4Nt0bk1MU7FBrCy+osJqhfVsOttT+K4XmeZ2vcQJ/vN/GM1ZWZB7HGoBDWGHxh9NSXmTUGxYcd5F88z4t+ZlFejshwH4MCPvw/N9xa+54D2SGyhBxPhpxOCjG8dLmk8wvWGCAeie94b43PRZtxVUJXIAZfGD31ZZQYghm3/eGEutJN5D9BKfPzGNRrDIGkT1dK/yzeChkilNA+pSstnuc16vWG+ALDkYU1Bo2KtdQfxNCIeVhWBrU7lbgIZFOqp3a0JPYLgxXoCMQQJxCDrkAMvko8epZOhtcYWr/HTPqeF14WiPzZ/PMY5DWG1igeLEPI7bT/QzkP+Nlrrtt61lNnrSJ4VCSIwfOkKdLYfNnpAQkzNau65JA+5muWGUAMcQIx6ArE4Ktco2e5lZMYOqfmAgmErksECw6d6Vr+73i88DxluzHEIDwWOhinY4fp9uzfCP9r7PgfeiJke4PYuj97gBg8r5iTavFN7z1i0H9yuq5ADHGqGjFARatco2e5ZWKNAeqoOsTQQTX/GpL47K26eHVGvvtD8VQv4bmf4k2koQ/UqlFSPeMqPhcr8Z4SYJV7E+OGWFckXWUb/nPAxOeKS02rb8w1JBBDnEAMUDqBGPQFYjCrqhCDuIISetBle9ko+FmkAfGqgeqqhOIDKUKzQqvi2k/AIeqbUMV91V3x4y/Sn0P3uzZcty7tK/RvoY/ZSttVdRFu22k17cmQYPjCBIghTr1FDLE3fAUM2r7lKllxN40lrcXJf5a/JSdBqb8YUDJ29pjioEi9U+HUI+nbCEEM+spPDAzH4JYtW0aPHk1EM2bM+MMf/sAcoydGWMN9Pvr0bnHKVH9MRJjGk4gh5v6G9h6lCxrCZukxnaFz+9A4Fx6D217FFYTIxZOOJ9xGUA6pLmF/ZOiLhAYxcJh7ixi8cBeNg4DkuV/4a+hKrMzZcgcMHQERpNW8ET3mv1W7lf9L8arM7jR40E/itxGCGPRVfmJYvny5H3Lx4sX//Oc/+WP0xAhrts/Ls6vwe+gYjRBDFzIItRshBuWoScHmGGLwOoNM7P2u0gO74l6mECnsFnEmpi6d4HFDNIiBydxzxNBWUg/RJgb5aFWQftf1ilY3Nnsjenivik0kzvGGdtpigy7fRghi0FeZiWHfvn133nmnn3DDhg22YvTECGueGOLXGAonhpjbWtVXJUKhg1sjoqOrYmvyGkMMMcTURdwkXLbAVQl+c6WJQSbqAHCVlxda2zPCanjyzvvFgOFn2EQ+qha1mdip30y3byMEMeirtMSwfv36iy66iIj+9V//9dChQ7ZipDVXgxjCS/2hZ1MUTQzSVQbxWR/qOx/rjniWH1zzFXcfvR0h9j4GcZhRTPZxdWm4jmJtF3c+WjH3MjEoZ0fPCxGDok8F06z6kn6ktYhN5Ipoq5qn++psMcQgPCg35De609aQINxx1FB9GyGIQV8lJIbf//73t99+ux/swQcf/Pvf/24lRjZzRYjBC30koJ72qkTw3+2jNgUxeKHxTL3EH2kn4lZ82iJkDe84ao0hBqnleqgukVZiI5uQ+I5Xrz/nMfcSMQjHierGw+j02YUYkiXyd/iqXhwxhJgiPOcrJ28FnsSuMYQ3KS9e5N5pZ2fhEjVC30YIYtBXqYjh3LlzQ0NDfqRvfOMbmzZtshIjj7k6xCDK8IJ69hTCnZVGp99iJC2Nmq0giCFOvUQMnifN+274gzmRydIYMYSmWd01BuUCgP4aQ2RiF04b4tcY8uxUedOGI38bIYhBX+Uhhl/84hdXXnmln2fNmjVehUYrhhim+7x4x7XycwBWJN8eWY5USoknj3H3ZuQSiCFOvUoM4WtfUvfWvY+hy2WJtGsMqjsopWk841UJ2SjdemFmpw3hE9VhqmrRmf83EIO+ykAMBw8evOuuu/wk99xzz9GjR/3tlRmtGGKY7/Pq1XqoLAIxxElNDM1m0/9Sy6a2UplTKWjZ89qzW0N1hSt000wJ1hjEpgxclRCVRAxZd9rQ/DZCEIO+/FrlOVLymIeHh2fPnu1nmDBhwvr16/WbMhjDoDmVDMZAn+83iUdu9fpzHrNSZV9jqLu11hfWRB5dKp9eK8RzH0PMHtWn++GX150YhHUEgzsVdiXdNEFCYTF66svWGsNrr70WsMLll1/+4x//OHqHY6pjsDhZOSc7cOBAqhjo8/0mrDHESZm5rMRQd4gcp/tdLorL8dFVgu6uJGJoK/Mag1qJ9zGIroS7FjLvNK4ktdC3EWL01Bc/MWzfvj34KMRVV1317LPPfvbZZ0pzPxPD0qVLJ02aNDQ0tHfvXp0Y6PP9JhBDnHqJGMQz4EQYKGaNIXzHRLDAHz8Xqz6YFDN5y58eUq0xiJcUCtmpcjkj3G7NbWD01BcbMZw+ffonP/nJDTfc4O9xypQp69evv3DhQsK/6B+D0e6j6FDSJs2DzB4xBIfSlVdeuWTJku3btyc0ZazPm7ypP/YjDaFPZebeRegMxvZHKOLr1x73gpPBfFFBDHHqJWKAyiAQg76KJoYvv/xy27Zt8+bNC+a/66677mc/+5lOy9rHoBYwRO4Zkik+TtaJIdBll122aNGinTt3Rkmr9MQQmiJNEUOhH1/Mpi45zH1CFMQQJxADlE4gBn0VRwz79+//wQ9+cNlllwUT3oIFC4aHh/Vb1jkG5cW84O6hMA4INv8eHyfEDokjd3mIIdDo0aPvv//+bdu2nT9/3vf3FjEYkvR59d4ihhSrXEqBGOJUNWIoc+a4ccdu5uCa7quvvqrTZhHEED5p7QyF6stPwYMp/Vks+KLN4HOn0vgZutaiffLhX4zq3E6bRWaJ4ezZs7/5zW+WL19+7bXXBsWYNWvWiy+++Mknn6RtWbc/R69UKa9dCTf0hJ41INQuYZIupwYGBubPnz88POz/ql/bhGKSeiUmXNGg3webFRcb2z05tKDTuigbeoZkXbyA2nGHn9Emtx5+7yIPaZSbULUQ2izdSC2/an8HiobDj5J0Qw+JjpROzpwdGcR3vMxzCr8ZxKCrChNDcAT713Rff/31BH/W0TM0sHVb5o6f18OfVWl9JNQfJ8UnVYgTl/Bj9AkcSTduxN5VqitlrVK9g6dOndq9e/ePfvSjmTNnirGvvfbap5566vjx45lb1uvPfhWUn80JV1B8qEeMR/WHUqtIYgijqPAYp9BHmVtf0NBZtRH+TeeqRN0h6Usno7sXk0QfEifnDj0wqe527rCKtODvMEjoBp/TVr3quvDtGnJzoVfZudGrY3GVlJPrwoT4jpd5TuE3gxh01Q/EEKiAa7qdISj5wniX+Tn4c6Pz7Zqdr9xUrFZ01Po6LfnEptuDvFrKsiqrrFXyO/jpp58eOXJk27ZtTz311KxZs8QEo0aNmjNnztq1aw8ePKj8X/P9ud6mscQP+3qecGeseIOy/ANLZo2W7V+ViFxGaP+l2zzX+bsuMajvbxA/b6WaZiPPcKtFmUXVbqiF6OpUzKuW/hJ6Pp/4DyF/tFNFHzwHYjBtBjHoqq+IoYDRUyKG6AVxovB3Y8gntMFW8Wym5mMD1Vy3Qwyu+AUk8gdg24NQt0u/XT6SqqUEYvjHP/7x8ccfnzhx4o033li/fv2yZcvmzJkzefLkaE1mzZr15JNP7tu375///Gfy7oz3DZmlnLpyGK6H6ivSCsAAACAASURBVC18tqeniOHyyy/nu/MxMp22u6PySr30PkRn0wRiCAFymBgUH+QOmo8lBq/zL7EfXWtzuNxTYl91dAfqMsnk0hkX2n8GMRRsBjEoNDg4GB1QIFH6RW4r9RqD+qJk56zXP5+lzjplcEdDaJwIfexK3NRtNEma5zTlx5k+ffq0adMmTZp01VVXjRkz5uKLLx41alRcYb/61a+OHz++Vqvdeeedg4ODjz/++NPacl1X37x8+fKunkj1o+sxwgDem8Rg59OVsWfbCpAVrwOErxuYIgbFoRB7VSIUK4G9Q6lVCeR6xBCDfOBKbYYWG3BVongziEEhE1NqxaVf5Lbk+xjEISmOGEKKcERwb2L4QQDhIajuOI7j765zx6T/O8V/oTmRU893s7Xneb3fkTpF9X9PuNmkETy+PU6OskT99QSnpPsYVFfuG65bD9850EFkQ8QQnnNj7t4R2qk78q2XCS0Ik3nMfQyqoMJvYhvCISt8HEf8T9z5WLwZxKCQcoAoeWalSnxVohG36qC3xqCc5VurDuFLD069BRGhW8o6zcbea23gsoQf64EHHli0aNGSJUuGhoZWrFixYsWKp556qohlA+NrDAK0yXOTfAYXqVbdoVrN7TpkWxk3rD0lOjQxij1YuSymIFrHVa4xhFbSUhKDJ8J5zMEgtqP8WISyBXFzJGnk/2OWNEK7C6OEduT0Et/xvp0HlQIxKKQcIEqeWanMxFDANV0VH0TZIcVVCeHW6vDNjjXXDb6KzBFvxVNccY0skhq6DupLWase6httCVclGsE6QmTBITzIJy15c2Q2WTpjxNBTEt7SfNMvm8JXz/IcvCCGOIEYFOpbYij405Wp72NQzO3+n+tOzXEUVxK8EEt47RaFgS+BECK/C79mo4fqEUNoW+Rze52LPpJRfksYMoMY8itye2Tey3TFKXS7UndQ7SYQQ5zUxDBSsJrNZtG7yCO/u0gbS55ZKZ3Mixcvnjhx4sMPP/zqq6/qtJmDGDpze+o7HwOJVyUUawyd67yqG+9SEkPn1CrjFQplRyqJdPpG6hecKJ7MRahfiaF/xXPkVmZOwRpDH60xBNd0NVtmW2NQK+k+htY5RnsZonMxN8VViTDUtO7UyrrGWZU1hgLVE+MGiKHfhDWGOOGqhEJ9RQxpWzY5eqpuX1ITQ/R2q0boS8hV7XauKHRZY1B9tFtqK9tV3MoQQ59nBjH0m0AMcQIxKARiSBBGT32BGMyaQQwQj0AMcQIxKARiSBBGT32BGMyaQQwQj0AMcQIxKARiSBBGT32BGMyaQQwQj0AMcQIxKARiSBBGT32BGMyaQQwQj0AMcQIxKARiSBBGT32BGMyaQQwQj0AMcQIxKARiSBBGT32BGMyaQQwQj0AMcQIxKARiSBBGT32BGMyaQQwQj0AMcQIxKARiSBBGT32BGMyaQQwQj0AMcQIxKARiSBBGT32BGMyaQQwQj0AMcQIxKARiSBBGT32BGMyaQQwQj0AMcQIxKARiSBBGT32BGMyaQQwQj0AMcQIxKARiSBBGT32BGMyaQQwQj0AMccK3XSvkdxdpY8kzK1VEZoye+lJ2pJII/Vlf6PP9Jp4jtzLHINYYsMYQK4ye+sIag1kz1hggHmGNIU64KqEQiCFBGD31BWIwawYxQDwCMcQJxKAQiCFBGD31BWIwawYxQDwCMcQJxKAQiCFBGD31BWIwawYxQDwCMcQJxKAQiCFBGD31BWIwawYxQDwCMcQJxKAQiCFBGD31BWIwa7ZLDFC/yXhHklSZYxDE0OkugUqeWSkQg10pa1XhvlG02VbmwcFBO1MWZE+Dg4PGO5KkyhyDIAYiEEOMlMWBlFLWqsJ9o2gzMvOY/czvvPMOEdVqNVsx+qTO1mOAGPKaQQwJAjHoC8Rg1ozMPGY/81tvvUVEt99+u60YfVJn6zFADHnNIIYEgRj0BWIwa0ZmHrOfee/evUQ0d+5cWzH6pM7WY4AY8ppBDAkCMegLxGDWjMw8Zj/zjh07iOj++++3FaNP6mw9BoghrxnEkCAQg75ADGbNyMxj9jP/8pe/JKIlS5bYitEndbYeA8SQ1wxiSBCIQV8gBrNmZOYx+5mff/55Ilq5cqWtGH1SZ+sxQAx5zSCGBIEY9AViMGtGZh6zn3loaIiIXnjhBVsx+qTO1mMYIIaRglXyb/n0B3ppY8kzK1Xct11D+jL+FhgR+jOPejfzzJkziWjXrl2242ipd+vcW1JmxhoDEdYYYoSn2aRS8ByYbO9gb/WNos3IzGP2M1966aVEdPbsWVsx+qTO1mPkzwxiIAIxWDJny3zw4MGDBw8ajIE688RAZp4YaTO/++67RDR16lSLMfqhzmWIAWLIawYxWDRny7xixYoVK1YYjIE688RAZp4YaTO/8sorRDR//nyLMfqhzmWIAWLIawYxWDRnyzxt2rRp06YZjIE688RAZp4YaTM//vjjRLRmzRqLMfqhzmWIAWLIawYxWDRnyNxoNPy3rNFomIqBOvPEQGaeGGkzX3fddUR06NAhizH6oc5liAFiyGsGMVg0Z8i8atUq/y1btWqVqRioM08MZOaJkcq8a9cuIpo8ebLdGJWvc2UygxhADNbMGTJPnz7df8umT59uKgbqzBMDmXlipDIvX76ciIaGhuzGqHydK5MZxABisGZOm/nIkSPipxmPHDliJAbqzBMDmXlipDJff/31RLR79267MSpf58pkBjGAGKyZ02Zes2aNSAwJ92qVJ3MZzMjMY+65zG+//TYRjRkzxm6MtOaeq7NXocwgBhCDNXPazDNmzBCJYcaMGUZioM48MZCZJ4a+edmyZUT02GOP2Y2R1txzdfYqlBnEAGKwZk6V+dChQ0Q0MDAgQsPRo0fzx0CdeWIgM08MTfPHH3/sH0THjx+3GCODubfq7KsymUEMIAZr5lSZV69eHX0w8zPPPJM/BurMEwOZeWJomv/jP/6DiObOnWs3RgZzb9XZV2UygxhADNbMqTLfeOONUWK45ZZb8sdAnXliIDNPDE3zlClTiGjLli12Y2Qw91adfVUmM4gBxGDNrJ/Zf/S9dEnC//Xdd9/NGQN15omBzDwxdMwvvPACEc2cObOHMgdCZh6zmhiazWaz2RwZGWlqK5U5lYqLEWf25x79dgqKkd+cSj2XOXhwU1SrVq3KGQN15omBzDwxupqPHTs2duxYItq4caN+s8ZjZDanEjJnNiuFNQasMVgz62eeNWtWHDHMmjUrZwzUmScGMvPE6Gp2XZeIvvOd73i9k1kUMvOYlZlBDCAGa2bNzCdPnozDBV8nT54sW+aiYyAzT4zqZX7vvff8o8b/ZpaeyCwJmXnMIAaFQAwWzdkyK9+yPDFQZ54YyMwTI9n84IMPEtHixYv9X3sisyRk5jGDGBQCMVg0gxh4zMjMYy5/5u3btxPRRRdd9P777/tbyp85KmTmMYMYFAIxWDSDGHjMyMxjLnnmzz77zP9E5YYNG4KNJc+sFDLzmEEMCoEYLJpBDDxmZOYxlzyz/0zob3/72+LGkmdWCpl5zCAGhUAMFs0gBh4zMvOYy5x5165d/oEjPVi9zJnjhMw8ZhCDQiAGi2YQA48ZmXnMpc380UcfTZgwgYiee+456U+lzZwgZOYxgxgUAjFYNIMYeMzIzGMubeZ58+YR0b333hs1lzZzgpCZxwxiUAjEYNEMYuAxIzOPuZyZ/W+cmjhx4unTp6PmcmZOFjLzmEEMCoEYLJpBDDxmZOYxlzDzb3/7W/942bVrl9Jcwsxdhcw8ZhCDQiAGi2YQA48ZmXnMZct89uzZqVOnEtFTTz0VZy5bZh0hM48ZxKAQiMGiGcTAY0ZmHnPZMi9cuDD6cUpJZcusI2TmMYMYFAIxWDSDGHjMyMxjLlXm//f//h8RjRs3LvqtK6JKlVlTyMxjVhPDSMFqNptF7yKP/OlH2ljyzEr1T2blW8am/qmzXSFzHr3xxhv+YbJ58+ZkZ3ky6wuZeaTMjDUGrDFYM2ONgceMzDzmkmT+8MMPp0+fTkRPPPFEV3NJMvdinfszM4gBxGDNDGLgMSMzj7kkmRcsWEBEs2fP1jGXJHMv1rk/M4MYQAzWzCAGHjMy85jLkHn9+vVEdNlll7333ns6/jJkTmtGZh4ziEEhEINFM4iBx4zMPGbrmQ8cOOAfHdu3b9ds2XrmDGZk5jGDGBQCMVg0gxh4zMjMY7ab+dNPP/VvX3j00Uf1W0adecyVyQxiADFYM4MYeMzIzGO2m3lwcJCI5syZ00OZs5mRmccMYlAIxGDRDGLgMSMzj9li5ueff56ILr/88vfee69XMmc2IzOPGcSgEIjBohnEwGNGZh6zrcwHDx4Ub1/oicx5zMjMYwYxKARisGgGMfCYkZnHbCvzzTffLD59oScy5zEjM48ZxKAQiMGiGcTAY0ZmHrOVzKtXryaim2++OVvLqDOPuTKZQQwgBmtmEAOPGZl5zPyZg49THjhwIFvLqDOPuTKZQQwgBmtmEAOPGZl5zPyZb7rpJiJavXp15pZRZx5zZTL3CzEUpzK/u/wxQAw8MZCZJ0aZMz/33HNEdMstt+RpGXXmMVcmM4ghr8r87vLHADHwxEBmnhilzXzq1KnRo0cT0Z49e/K0jDrzmCuTucrfdu2/woZbq7kN8TVHt8ib6g459WityyyLdc6sbJl9YjCdRVf9U2e7QuZkPfTQQ0R033335WwHdeZRZTJXfo1BCxjC2xpujQRF2KHwzFnMtuucxYw1Bh4zMvOY2TIfOnTIPwr+8pe/5GwZdeYxVyZzlYmh7lBINdeVthA5ddHm1D2v4TohdugCDKV+d/ljgBh4YiAzT4xyZr7nnnuiNzxmaxl15jFXJnOVicHzPK/uyOsJ0S2esMYg/lV1bSJjjOLNlemRXVsGMaQ1IzOPmSfz4cOHiejiiy+Oa6GEmc2akZnH3IfE0HBr/iKCU/eiaw6thYeGFxCDdD0i7GHKnNFcmR7ZtWUQQ1ozMvOYeTL7dzCsXLnSSMuoM4+5MpkrTQwtVBDXCpQrDO1bF2puo2OI/sCSOau5Mj2ya8sghrRmZOYxM2Q+efKk3//ff/99Iy2jzjzmymSuMjHIKwbCcoOo1tKDxAedeyFBDOXKDGJIa0ZmHjND5pUrVxLRQw89ZKpl1JnHXJnMVSaGtjqYELnq0GaBKB+AGIo3gxh4zMjMY2bIPGXKFCLavHmzqZZRZx5zZTL3CTEobkcIfQ7C54OY2xj8BQrezKnNJahzajOIgceMzDxm45nPnDnzySefBGb/W62nT5+ev+VAqDOPuTKZq0wM0scm25v8j1CGPzYZeUZD3aFaze3+SYlyv7v8MUAMPDGQmSeG9cyTJ08eO3bsDTfcsHDhwltvvZWIFi5c+Ne//vXLL7/M2bIv1JnHXJnMVSaGtoSrEo1gHSGy4BBsaLi11p/bfGEhc2pzCeqc2gxi4DEjM4+5iMx33323cs1zYGDg61//+iuvvJK5ZV+oM4+5Mpn7ixhC2zr3OrYIogUTkcUGcZGCK3NqcwnqnNoMYuAxIzOPuYjMq1atirtQetNNN+Vp2RfqzGOuTOZ+IIZiVeZ3lz8GiIEnBjLzxLCe+Ve/+lUcMQwPD+dp2RfqzGOuTGYQQ16V+d3ljwFi4ImBzDwxrGc+duyYEhcS7n+0nrloMzLzmPuaGOLMyumn5JmV6p/MIIa0ZmTmMReU+Wtf+1qUGDZt2pS/ZQ915jJXJnOVv+1aR/7hJ20seWal+iez8i1jU//U2a6QOdCNN94o4cKkSZNMNY4686gymbHGgDUGa2asMfCYkZnHXFDm73//+xIxrF+/3kjLHurMZa5MZhADiMGaGcTAY0ZmHnNBmV944QURF6644ooLFy4YadlDnbnMlckMYgAxWDODGHjMyMxjLijz/v37RWJ49tlnTbXsoc5c5spkBjGAGKyZQQw8ZmTmMReU+eOPPw5w4dJLL/30009NteyhzlzmymQGMYAYrJlBDDxmZOYxF5d57Nixfrd/6qmnzLaMOvOYK5MZxABisGYGMfCYkZnHXFzmq6++mogGBgb+9re/mW0ZdeYxVyYziAHEYM0MYuAxIzOPubjMjz/+OBGtXLnSeMuoM4+5MplBDCAGa2YQA48ZmXnMxWV+6aWXiOiDDz4w3jLqzGOuTGYQA4jBmhnEwGNGZh5zcZn/8Ic/DA0NFdEy6sxjrkxmEAOIwZoZxMBjRmZ98+DgIEHaGhwcZHhTJKE/85hBDAr5/V7aWPLMSvVPZuVblicG6swToycy25t8e1UMb4ok9GceM4hBIWWnL3lmpfonc/I4lSEG6swToycy6/QuyBeIQd9cmcwgBhCDNTOIgceMzPpmEIO+QAz65spkBjGAGKyZQQw8ZmTWN4MY9AVi0DdXJvNXms1ms9kcGRlpaiuVOZWKixFn9ju9fjsFxchvTqWezqzzlpUts11zKvV5ZhCDvpKPxOr1jTzmVCpzZqwxYI3BmjlbZqwxpDUjs74ZxKAvrDHomyuTGcQAYrBmBjHwmJFZ3wxi0BeIQd9cmcwgBhCDNTOIgceMzPpmEIO+QAz65spkBjGAGKyZQQw8ZmTWN4MY9AVi0DdXJjOIAcTQEp52Z1wLFizI+aYYMfdnf85m9t84/db6Wcm1ql7fyGOuTGYQg6LTlzyzUvkz25lUq66cb4oRc3/252zmtO9aPyu5VtXrG3nMlckMYlB0+pJnVsoUMeg3AiULxJDHDGIov0AM+ubKZAYxgBhawlhpViCGPGYQQ/kFYtA3VyYziAHE0BLGSrMCMeQxgxjKLxCDvrkymUEMIIaWMFaaFYghjxnEUH6BGPTNlckMYgAxtISx0qxADHnMIIbyC8Sgb65MZhADiKEljJVmBWLIYwYxlF8gBn1zZTKDGEAMLWGsNCsQQx4ziKH8AjHomyuTGcQAYmgJY6VZgRjymEEM5ReIQd9cmcxfGSlYzWaz6F3kkd/ppY0lz6xU/swYK81K2bWsqD/7czbhKNCXrR6O/swjZWasMWCNoSWMlWaFNYY8ZqwxlF9YY9A3VyYziAHE0BLGSrMCMeQxgxjKLxCDvrkymUEMIIaWMFaaFYghjxnEUH6BGPTNlckMYgAxtISx0qxADHnMIIbyC8Sgb65MZhADiKEljJVmBWLIYwYxlF8gBn1zZTKDGEAMLWGsNCsQQx4ziKH8AjHomyuTGcQAYmgJY6VZgRjymEEM5ReIQd9cmcwgBhBDSxgrzapHiaE49cS4gaNAXyAGfXNlMoMYQAwtYaw0KxCDpJ4YN3AU6AvEoG+uTGYQA4ihJYyVZpWBGE6ePKlv1m85AzE03FrNbSRvkTfVHXLqCW32xLiBo0BfIAZ9c2UygxhADC1hrDQr/Xq+++6769atu+222/TrXzAxaAFDeFvDrZEgBTv0xLhh+CioOxSvoHgNtxb8ItVRtHlew60Jha07FHlPYlLU3IZXd0jTrykQg765MplBDCCGlsyOlRqjmThO5t9b8vmtnhpuLaGZlHm71vPo0aPPPPPMLbfcIk4Pmo0X1zfkOa7mupFZz6mLNqfueQ3XCbGDqopWxo25c+euXbt2375958+f14lhi5sD+pLQLPyrSAxRGIntuj4xRBvPKRCDvrkymUEMIIaWOIghNOkaIwZhOTwfOiQTg5firM6Lr+eRI0fWrFkzY8aMYKQfGBgoDzF4njDDJGzxhPlH/Gv8tQkr48bUqVOD2s6cOXP58uWbN28+depUnN8WMQSKI4YQILQxLqh0u+cmrml0R4t0AjHomyuTGcQAYmiJnRgMKTTEFkwMaU7SpHo2Go1Vq1ZNnz5dCQplIgb/RLY98yvnoNDpcHQdXfCwZE5oeebMmcpJ87rrrnvooYd+/vOfHzt2TPSbPQo0FKpezW0krjG035TW1tZB1vWo6naHSUaBGPTNlcmMb7smwrddj4yMpPieX39eDWaS0KlNMHAJxFB3/D+Ic4800Gk16YnTl7hSITQSqN1EZ6QUfvPDdRprm8LEoNhZGmTw//WNN94YGhq65pprlPNWCdWeXsRZRrnC0C64P3W1DPIPtl9NRum8v10VQ1Jhhbqbmr/CPEyKI0Lj+l9xxFAx7dixw8iALKkycwrWGIiwxuB5XoqzK39M84eg1vjWmYkpmDH8n8K3W8VdldBtMkQhviN8/hSChCRi6OxBaFcgBvXOvBAKJcuv58GDB1esWDFt2jTecS+75BlLWG4QFZ682qDQ4SkQQ7haAkqruk+n58WvMTRcx3H85Z/2jYwk3FMS6tuiam7d6O0LgZjfFB49/fTTmi+/SvOgUrgqoZDfS6SNJc+sFDMxxNyM0JlO644/UoUvEiQRQ5cmpaVXYbN8yqVHDEKsoOlg3I7ZWcuchhg6O4lclVBKo2HPK/7TlSKJRc56hdv6uxEDT+ag5ePHj2/evHnZsmU33XST/iRx3XXXbd261W8h1bugK7HPRAvTqa9/D6maGBpurebWW4sFdYfkq2+N8LGmvpphVsm1KvNYp9TTTz8NYhAFYlAIxBAoFTGEbrlS3UbQPtWJDmtxxNClScUar2KA1SaG0LAtUklADIqdtdJlIoZA0TsfRWk07HlMxBDCA1/yG+VfeI+dix2ezLt37/7BD34wbtw4afe33XbbHXfcEZuOaMmSJW+99ZbYVKp3QVfhjzhI3afu+FfIWos5bsxVibrj1IWGFFUPtRv5jEUBiwzJtSrzWKcUiEESiEEhZacveWalSkgMrVsFpDOmPMSgGvUyXpWQUwWgoFpjkPaXjxgClf7TlfIHUOQbQyPzUN2hWs2NO5k1nvmjjz76r//6rxtuuCFIPHXq1AceeGD9+vWHDh364osvPM/bunVrFBSuvfbadevWffLJJ9E2iyAG6d7cmCW5DgrofLpSrn2X30MXN0zRA4hBP0Zl5hQQA4ihpSKIoXUupL7gkI4YpGXXmI+YhyBB/A/xTonw7WKCK3Q1WbWz9Hc+6jjL9wQnLzSHNYJ1hMiCQ+jN7Ny5olwCN5j51Vdfve+++wICmDx58urVq48fPx517t27V2SF73znO1u2bElo2TwxSJyl/iRq0cQgpDB3hQLEoB+jMnMKiAHE0FIxxOCpJuuYz0p0aTK0FBt3xbZtCd1FSe1PsItrDPXQ5WP1K1PtTHe8TTv3jJTpKdHqiUW6DbX9acAoS3TuyjOa+W9/+9uqVasmT54cEMCCBQt27dqV0PLbb79NRJdccsljjz129OjRrjHMEkOYlz3Pi3t4SBZikFdOIoQQKLj3wV/2M3WBAsSgH6MycwqIAcTQkvmzKy6lPmvKPGqaeIJTnErSN4pTzszPPffcJZdc4lf1pptuev7558+cOdO15VOnTm3YsOHzzz/XjGHwKIh5KrP8IeP2RuHTxVF1yLrLGoP69pPAE+W4HAIx6MeozJwCYgAxtNS7xJD6wU3ZiCHh5gaVQAySMmfetm1b8BmTBQsWvPnmm3la5lxjqLZADPoxKjOngBhADC310VhpcGU2Xj1KDKXqz2+++eZdd93lV3LmzJm7d+8uOkYfHQW5BWLQj9G7x6AkEAOIoSWMlWYFYshjfvPNNx9++GG/hhMnTty0aRNPDBwF+gIx6MfoxWMQxKAQiCEQxkqzAjFkNr/wwgujRo3yC7h69WrxLoSiY+Ao0BeIQT9Gzx2DHohBuR3EEAhjpVmBGDKYv/zyy8WLF/ule/DBB0+cOMEcA0eBvkAM+jF66BgMBGJQCMQQCGOlWYEY0pr//Oc/33zzzUQ0evTo9evXW4mBo0BfIAb9GL1yDIoCMSgEYgiEsdKsQAypzJs3b/avRMycOfPo0aO2MuMo0BeIQT9GTxyDkvBt1wr5nV7aWPLMSjF+2zWkJWXXsqLy9+dly5b55Vq0aJG/xVZmHAX6stXDC+obrusSkeu6RTRe/mMwKnzbtUJYYwiEsdKssMagY37//fdnz57t1+rFF18MtmONofzCGoN+jDIfg3HCVQmFQAyBMFaaFYihq/nIkSMTJkwgouuuu056LhOIofwCMejHKO0xmCAQg0IghkAYK80KxJBsPnLkyPjx44lowYIFn376qWQGMZRfIAb9GOU8BpMFYlAIxBAIY6VZgRgSzIcPH/ZxYeHChUoziKH8AjHoxyjhMdhVIAaFQAyBMFaaFYghznz48OGrrrrKX12IM4MYyi8Qg36Msh2DOgIxKARiCISx0qxADEpzgAtxqwu+QAzlF4hBP0apjkFNgRgUAjEEwlhpViCGqPnw4cNXXnllV1zwQAy9oIoRQ1qVJDOIgdUMYgiEsdKsQAyS9u/f7+PC/fff39UMYii/QAz65pIcgyCGvGYQQyCMlWYFYhB15syZa665RhMXPBBDL6gXiaHh1qQvuo9ukTfVHXLqXVtOVhmOwbRmEINCIIZABBWgnG+KEXMZ+vN3v/tdIpo7d66mH8RQfiXXqpT9WQsYwtsabk08oDvs0HPHYFoziEEhZacveWal8mceHBxknEn7QgmfBdB8U4yYrffnlStXEtHkyZNPnz6t2TKIofxKrlXZ+nPdCR+cNdeVthA5ddHm1D2v4TohdhAWG3rrGMxgBjEopOz0Jc+sFDLzmJE5rfmll17yj7LXXntNv2UQQ/nVW8TgeZ5Xd+T1hOgWT1hjEP8auTbRQ8dgNjOIQSEQg0UzMvOYLWZuNBr+Ifbiiy/2RGYQg756jRgabs1fRHDqXnTNobXw0PACYpCuR4Q9vXMMZjaDGBQCMVg0IzOP2VbmkZGR6dOnE9EjjzyStmUQQ/nVY8TQQgVxrUC5wtC+daHmNjqG6A+90Z/zmNXE0Gw2/S+1bGorlTmViosRZ/Y7vX47BcXIb04lZM5sTqU+z/ztb3+biG6//fYMLaeSwcwgBn0lD55l68/yioGw3CCqtfQg8UHnXsjWFp7Mds1KYY0BawzWzMjMT8O9zgAAIABJREFUY7aSefXq1UQ0fvz4YO/lz+xhjSGNemyNoaUOJkSuOrRXDyJ8oNjSI/05j1mZGcQAYrBmRmYeM3/mN9980z+y9uzZk61lEEP51bPEEMIDX6HPQfh8EHMbg79AwZvZjhnEoBCIwaIZmSGod9VbxCB9bLK9yf8IZehjk9FnNNQdqtVc6QJGScYNEAOrGcRg0YzMENS76i1iaEu4KtEI1hEiCw7BhoZba/25zRcWMtsxgxgUAjFYNCMz1FuKXaXuY8XVqqzHYNztjsF9CkREnYsSkcUGohY4lGTcADGwmpWdvuSZlUJmHnMpiUE++8muhlsLxsjwXeOsaq8gm3lR6h0kvK72mWfwuf12DtapuBc0ODgYV8TKH4MlyQxiYDX7/V7aWPLMSiEzj9l4ZnGOTqEQJJgiBnE11hyFaCm8u+QJ3fMyl01/B9I99TqlKK4/P/DAA0S0detW4y3jGMyskmQGMbCaQQwWzcjsmSEGQxLXa3POyFn2XVpiiP32QknF9ef77ruPiF555RXjLeMYDPT0008T0dNPP228Za9CdQYxgBismfsxc3DHduRJte27q2puXXjkHNXcRsflT1uhj321HmPXntGEBlptdNzCpCc0oZgWI7HCm6SZXYrbfeedtoIPu0uNd5nQ5XxhfBB+U9dP3oGqGBIlaCFDcf3Z/+ZPzS/mwDGYrWUQgyQQg0IgBovmvsscukvA9X8Iz3Z1h0j+NfRhMPEmA9VVCbGB4Cau9n5E4hBW3OWH1ERiiTsO/bcqbpede3Wn6+tJu8aQRAzq+nV2oC6GJ0GC1oWJ4vqz//TM3bt3G2+5747BeIEYJIEYFAIxWDT3XWZxdhK3hedc6WxcWhkQ5sI4YlDf3xBMeuFGxc3qXUUuAHSmz6S46p2HJIBBccSgfFHBDmKKEamHziJDcf159uzZRLR//37jLffdMRgvEIMkEINCIAaL5n7MHHniXIQYxKlSnjk7vycQQwx/BDuKfmlfe3M8MYRmS8XEq4ir3nlk9yrSMUkM6vqFiEFRDK9cxHD77bcT0aFDh4y33I/HYIxADJJADAqBGCya+zezdIlAlxiEuTsnMShmv9irEslrDCmJQWttpChiUASPv4O0TFclbr75ZiI6fPiw8Zb79xiMCMQgCcSgEIjBornvMncu4XdmoG7EEPp+nOgDbuVfdE7z5dsSOv8aniLj72MQzsOzE4N/w6EJYojcERFaz1DVL7xYoyhGue58nDFjBhH9z//8j/GW++4YjBeIQZKaGEYKVrPZLHoXeeQTg7Sx5JmVQmYe5ckcc1d+XXhSkmoKDj58ID/7PtiWkhg6+wzdtxiaFuWFBWH1Pvwf2a9K1FxXsMhPTRKjKulBLJuYz3GlNQZV/SJJIy8tVI22vbi+kazp06f7awzGW+63YzBBrusSkeu6RTRemTpjjQFrDNbMyNxN3c+1jSryDTy2FL4lMnumPP/cIQb7T3C65ppriOjkyZPGW8YxGAhrDJJwVUIhEINFMzJ3EzMx8D+4KUbts36nng9iMtYv9inRySqub0yePJmITp06ZbxlHIOBQAySQAwKgRgsmpG5m9iJoWLirV9xfWPs2LFEdPbsWeMt4xgMBGKQBGJQCMRg0YzMPOaCMn/jG98gouHhYeMte6izoC+++IKIRo0aZbxlD3UWBGKQBGJQCMRg0YzMPOYiMr/22mtEdO211xpv2RfqHOj06dNENGHCBOMte6izIBCDJBCDQiAGi2Zk5jEXkXn+/PlEtHbtWuMt+0KdAx07doyIrr/+euMte6izIBCDJBCDQiAGi2Zk5jEbz3zixAn/wHn//ffNthwIdQ504MABIpo9e7bxlj3UWRCIQRKIQSEQg0UzMvOYjWdetWoVET388MM9lJnBXFDmX//610S0cOFC4y17qLMgEIMkEINCIAaLZmTmMRvPfOWVVxLRoUOHeigzg7mgzBs3biSioaEh4y17qLMgEIMkEINCIAaLZmTmMZvNvHnzZiKaNWtW2hioc7aWn3nmGSJas2aN8ZY91FkQiEESiEEhEINFMzLzmM1mvuOOO4jopZdeShsDdc7W8mOPPUZEGzZsMN6yhzoLAjFIAjEoBGKwaEZmHrPBzG+99RYRjRs37v/+7//SxkCds7U8ODhIRNu2bTPesoc6CwIxSAIxKARisGhGZh6zwcxLliwhoieeeCJDDNQ5W8t33XUXEf3ud78z3rKHOgsCMUgCMSgEYrBoRmYes6nMn3/++Ve/+lUievfddzPEQJ2ztXzDDTcQ0dtvv228ZQ91FgRikKQmhpGC1Sz3t3z6xCBtLHlmpZCZR/2c+aWXXiKiO+64w0hryernOku69NJLieiDDz4oonHUORC+7VqSMjPWGLDGYM2MzDxmU5kXLlxIRD/96U+zxUCdM7TsPyL6iiuuMN6yL9Q5ENYYJOGqhEIgBotmZOYxG8n8j3/842tf+5p/spstBuqcoeVGo0FEt9xyi/GWfaHOgUAMkkAMCoEYLJqRmcdsJPP27duJ6Jvf/GbmGKhzhpa3bt1KRAsWLDDesi/UORCIQRKIQSEQg0UzMvOYjWS+//77ieiFF17IHAN1ztDy2rVrxQ+nGGzZF+ocCMQgCcSgEIjBohmZecz5M//jH/+46KKLiEhqqsyZ+c1FZH7ooYeI6MUXXzTesi/UORCIQRKIQSEQg0UzMvOY82d+5ZVXopck0sZAnTO07D9h84033jDesi/UORCIQRKIQSEQg0UzMvOY82d+4IEHiOgnP/lJnhioc4aWJ02aRETHjx833rIv1DkQiEESiEEhEINFMzLzmHNm/uc//zl69GgiOnnyZJ4YqHPals+fP09Eo0eP7qHMDGYQA48ZxKAQiMGiGZl5zDkz79ixg4juvPPOnDFQ57QtHzt2jIhqtVoPZWYwgxh4zCAGhUAMFs3IzGPOmXloaIiInnvuuZwxUOe0Lf/mN78hou9+97s9lJnBDGLgMYMYFAIxWDQjM485Z+ZarUZEb731Vs4YqHPaltesWUNEq1ev7qHMDGYQA48ZxKAQiMGiGZl5zHkyf/DBB0Q0ZsyY/DFQ57Qtz5s3j4h+/etf91BmBjOIgccMYlAIxGDRjMw85jyZt2zZQkT33ntv/hioc9qWJ0+eTETvvfdeD2VmMIMYeMwgBoVADBbNyMxjzpN58eLF0Uc9ZouBOqdq+aOPPiKicePGpY2BOmdrGcQgSU0MIwWrWe5v+cS3XVsUMvMoT+arr76aiP77v//bYB4d9Vudo/r1r3/tf0TFYJtRoc6B8G3XkpSZscaANQZrZmTmMWfO/O677xLRxIkTjcRAnVO1/OyzzxLRypUr08ZAnbO1jDUGSbgqoRCIwaIZmXnMmTNv2rSJiAYHB43EQJ1TtbxgwQIievnll9PGQJ2ztQxikARiUAjEYNGMzDzmzJn9h0P/4he/MBIDdU7V8te//vXg+dC9kpnHDGLgMYMYFAIxWDQjM485c+YJEyb49+obiYE667d89uxZIrrkkksyxECds7UMYpAEYlAIxGDRjMw85myZjx8/TkTTpk0zFQN11m953759RDRr1qwMMVDnbC2DGCSBGBQCMVg0IzOPOVvmrVu3EtHChQtNxUCd9Vtet24dEf3whz/MEAN1ztYyiEESiEEhEINFMzLzmLNlfvzxx4no2WefNRUDddZv+e677yai7du3Z4iBOmdrGcQgCcSgEIjBohmZeczZMs+ZM4eI9uzZYyoG6qzfsv/14mfOnMkQA3XO1jKIQRKIQSEQg0UzMvOYs2UeO3YsEZ0+fdpUDNRZs+WDBw8S0Y033pgtBuqcrWUQgyQQg0IgBotmZOYxZ8h84sQJIpo8ebLBGKizZsv+1LVixYpsMVDnbC2DGCSBGBQCMVg0IzOPOUPmHTt2ENG8efMMxkCdNVuePXs2Eb322mvZYqDO2VoGMUgCMSgEYrBoRmYec4bM/jP2f/zjHxuMgTrrtPz3v//dH5T+93//N1sM1DlbyyAGSSAGhUAMFs3IzGPOkHnu3LnSaW7+GKizTsu7d+8Wn8SQIQbqnK1lEIMkEINCIAaLZmTmMWfI7D/t8YMPPjAYA3XWaflHP/oRET355JOZY6DO2VoGMUhSE0Oz2Ww2myMjI01tpTKnUnEx4sw+Mei3U1CM/OZUQubM5lTq0cyHDh0ioiuvvNJsDNRZp+Ubb7zR/wKqzDFQ52wtL1++nIiWL19uvOW0KnOdscaANQZrZmTmMafN7C+Mz50712wM1Llry/7XSYwaNeqLL77IHAN1ztYy1hgkKTODGEAM1szIzGNOm3nDhg1ENDQ0ZDYG6ty15S1bthDRXXfdlScG6pytZRCDJBCDQiAGi2Zk5jGnzbxixQoiev75583GQJ27tjx//nwi2rhxY54YqHO2lkEMkkAMCoEYLJqRmcecNvO8efOI6NVXXzUbA3VObvmzzz7zh6OPPvooTwzUOVvLIAZJIAaFQAwWzcjMY06b+frrryeit99+22wM1Dm5Zf+SxLe+9a2cMVDnbC2DGCSBGBQCMVg0IzOPOW3miy++mIg+++wzszFQ5+SWHcchop/97Gc5Y6DO2VoGMUgCMSgEYrBoRmYec6rMx48fJ6Lx48cbj4E6J7T82WefDQwMENGpU6dyxkCds7UMYpAEYlAIxGDRjMw85lSZlc8cNBIDdU5oeevWrXGXJNLGQJ2ztQxikARiUAjEYNGMzDzmVJk3btxIRA8++KDxGKhzQsv33XcfEb344ov5Y6DO2VoGMUgCMSgEYrBoRmYec6rMq1at0vkOqgwxUOe4ls+fPz9q1Cgi+vDDD/PHQJ2ztQxikARiUAjEYNGMzDzmVJkXLFhARFu2bDEeA3WOa/nll18mon/7t38zEgN1ztYyiEESiEEhEINFMzLzmFNlvvXWW4nozTffNB4DdY5r+Z577iGiDRs2GImBOmdrGcQgCcSgEIjBohmZecypMk+bNo2ITpw4YTwG6qxs+Z133vG/S+LcuXNGYqDO2VoGMUgCMSgEYrBoRmYec6rMY8aMIaKE2StzDNRZ2fITTzxBREuWLDEVA3XO1jKIQZKaGEYKVrPZLHoXeeQTg7Sx5JmVQmYeVTvzxx9/7J/vFppHR9Wuc6CzZ8+OHTuWiH73u98VEamr+qTOOnJdl4hc1y2i8crUGWsMWGOwZkZmHrN+5o8++oiIJkyYUEQM1Dna8ksvvUREd9xxh8EYqHO2lrHGIAlXJRQCMVg0IzOPWT/z0aNHiej6668vIgbqHG151qxZRDQ8PGwwBuqcrWUQgyQQg0IgBotmZOYx62f+/e9/T0SzZ88uIgbqLLV86NAhIrrqqqvMxkCds7UMYpAEYvA8zztw4MBSQT4xiFsOHDhQtsw6QmYec7Uz79ixg4gWLlxYRAzUWWr5+9//vn/h3GwM1DlbyyAGSSCGliZNmkQxmjRpUjkzdxUy85irnflnP/sZEQ0NDRURA3UW9f777/tjjs4HWUuSuRfrDGLIbAYxtDQ0NBRHDP5YWcLMXYXMPOZqZ167di0RrVmzpogYqLMov9T33nuv8Rios37LHwlauXIlEa1cuVLcmLllSZWpcz8Sw969e+OIYe/eveXM3FXIzGOuduZHH300+eGDeWKgzoEuXLgwceJEItq1a5fxGKizfst+h4/To48+mrllSZWpcz8Sg+d5V1xxRbR/jBs3zv9rOTMnC5l5zNXO/L3vfY+Itm3bVkSMVGao11WG/ty15X379iUQw759+zK3LKky40afEsOSJUui/SN47Fo5MycLmXnM1c48f/58/RPftDFADH2lMvRnnZbHjx+vxIWun2Hpz3GjT4nh9ddfj3aR119/3f9rOTMnC5l5zNXOfPfddxPRb3/72yJigBj6SmXozzotB5+Yk7R06dKcLYuqzLjRp8Tged5ll10m9o/LL788+FNpMycImXnM1c48Z84cIjpw4EARMQojhoZbIyIipx7dXnMbJnZRd4Lm4/bGoPauDb0o9Q4SXlfd6ey+a3HL0J91Wt6zZ4+SGPbs2ZOzZVGVGTf6lxgWLVok9o9FixYFfypt5gQhM4+52pn9RxA2GroTEg8xJE9Oob/G/5JHdSeYRw1SiI7Cu+syoXteCG0y7q7LfzfcWjtQ3UkqRRn6s2bL/ld7iBozZoyRlgNVZtzoX2LYuXOn2EV27twZ/Km0mROEzDzmame+9dZbiejIkSNFxCiIGEJzZBETujBH5p2Rs+y7vMQQroysMvRnzZYXL14sEcPixYuNtByoMuNG/xLDhQsXRo8e7feP0aNHX7hwIfhTaTMnCJl5zNXOPGPGDCI6duxYETF0za2179bMF/zWXg6vO1Rz626NiGqOU+v81XEkqzDd+lNhqOXgF3HS6+xNMS22Lwp0woU2yTO735ZT19250JbfeOSld5nQI/nC+ND5zW+nYw+tyoT/QZUy9EJjkaEM/Vmz5ddee00ihtdee81Iy4EqM2709bddB0OM4zji9jJnjhMy86jama+99loi+tOf/lRongSFTqrrbmemDp/li1NY/BqDRAzSLN/6n7oTtCb8KLQqXJKQ9tZwa8Kuhf8W96a784brtHYtNptvjSGJGDovVdifsAN1MWRICC5McHYS48fguXPnLr744gAXLr744nPnzpndRWXGjf5dY/A8b9u2bX4XkT6AXubMcUJmHnO1M0+dOpWI9P3mM4dn4c62+OsC2sQQc39De9KTLmgIm+VFCGFODSUNpk+pKY2dR15v4cSgfFHBDmKK4cn1SFhkKEN/1m95cHAwIIbBwUGDLfuqzLjR18Rw/vz5gYGBgYGB8+fPi9vLnDlOyMxjrnZm/ytXTp8+XUQMXXN7MVxzktUmhpibHdr/H1rT76zsJxJDeLaMTryd/XXZeWT3KtIxSgxiOyFQCH5QFMOrLDH438Hma8eOHQZb9lWZcaOvicHzvPnz58+fP1/aWPLMSiEzj7namf1noZ47d66IGKnM8lo5DzGoZr/YqxLJawzpiEFrbaQwYogGj7+DNOaqRFRl6M/6LX/++eejRo0iolGjRn3++ecGW/ZVmXGj34lheHh4eHhY2ljyzEohM4+52pn9h5R89tlnRcTQMtcd8S4BTmKI3JbQ+deYuyai9zEI1xKyE4N4o0Y+YpDviAivZ7Rt0g0YgiVajGre+ehrypQpRDRlyhTjLXsVGjeMEYN4HQgqWtErbZXpkfwxrGTG8aKU4hMDnhd+dFAiMYSsKYnBi9l50qcrxdX70H/kuCrhuKJFfmpSFKjCarcjt1pzXWmNQfF5jUjSyEsLVSORYMp/DEraunUrEW3dutV4y16FxjpjxGB03IC6S+fdTfUO8pv7ObPt7lNS6deWU+ELE/Yk3RKZOVOef25U8AlOvj799FMi+vTTT4237FVorDNMDPqNQJmlLHVleiR/DIvEoN8UZFXMD26KUfusv+Y2ckFMVmKo5FOiRT3yyCMFtVyZsQ7E0HsCMVg0gxignleuBYpY4UJbGZT80VAQQz9KWep+nn05zSAGfnWtVTn7hv+1XkNDQ8wxbB2D7JMjpFbmd1ASiKEiUpa6n2dfTjOIgV9mx0GevvHII48Q0fTp0+Oui5cwc86W0aWtC8QAKQRisGgGMfCr54jhxRdf9DMnfAto2TLnbxld2rpADJBCIAaLZhADv3qLGPbv3+8Hfumll6zEADH0rUAMkEIgBotmEAO/eogYTp8+ffXVVxPRypUrbcUAMfStQAyQQiAGi2YQA796iBjmzp1LRN/97nctxgAx9K04iGHEkNBd2OSXWqp/Zb5NteQylRnHi76UHb6Eevjhh4no6quvPnHihO0sujJ4DKJLW5fZI6XYb7tGd2ET1hgsmrHGwK+eWGP45S9/6ec8ePCgxRhpzVhjqJJwVQJSCMRg0Qxi4Ff5iWHLli1+yE2bNlmMkcEMYqiSQAyQQiAGi2YQA79KTgwBLjz22GMWY2QzgxiqJBADpBCIwaIZxMCvMhNDgAs//vGP+7k/e+jSJRCIAVIIxGDRDGLgV2mJYXh42M/29NNPe/3dnz106RIIxAApBGKwaAYx8KucxCDhgtff/dlDly6BQAyQQiAGi2YQA79KSAybN2/2U61duzbY2M/92UOXLoFADJBCIAaLZhADv8pGDBs3bozigtff/dlDly6BQAyQQiAGi2YQA79KRQzLli3z8zzzzDPSn/q5P3vo0iUQiAFSCMRg0Qxi4FdJiOH48eO33347EQ0MDCi/Zaqf+7OHLl0CgRgghUAMFs0gBn6VgRi2bdt28cUXE9GNN9545MgRpbmf+7OHLl0CgRgghUAMFs0gBn5ZJ4ZVq1b5GR566KEvvvgiztzP/dlDly6BQAyQQiAGi2YQA78sEsPx48f9r6MkovXr1yeb+7k/e+jSJRCIAVIIxGDRDGLgly1i+OlPfzp27Fgimjp16v79+7v6+7k/e+jSJRAHMTSbTf9LLZvaUprRXdjkl1r//dJ8B/nNqVSxzDhe9NW1wxt/uzdt2lSr1fz9fvvb3/7zn/+s377BGAzmVEpuGV3ausweKUphjaH3hDUGi2ZTmXG86ItzjeHIkSPz5s3z93jDDTcMDw/rt9zP/dlDly6BcFUCUgjEYNEMYuAXDzGcOXPmkUce8fc1duzYn/zkJ2lb7uf+7KFLl0AgBkghEINFM4iBX0UTw5kzZ9atW3fJJZf4O3r88cfPnTuXoeV+7s8eunQJBGKAFAIxWDSDGPiVdhzcv3//k08+qWPetWvX/fffT20tWLDg6NGjCS0nq5/7s4cuXQKBGCCFQAwWzXGZDxw4kKplHC/60hwHv/jii02bNt16661E9MADDySY//KXvzz55JPXXHNNwArz589/44034lrWVJX6c4aW0aWtC8QAKQRisGiOy7x06dJJkyYNDQ3t3btXp2VDx0vdoZrbiPljw62F/1p3iJx6QnMNtxbbXN0R/tRwa8ktCUan7jXcWpc9J6lrrf74xz+uXLlyzJgxJChq+/vf/7558+bZs2cHnuuvv37dunUffvhhXMt9258ztIwpwLr6mRi6Dm7aioybFhQ/sGd4nSAGi+YEYgjmoSuvvHLJkiWvv/46xxpDbAdquDVxAiXHlTb4/1Z3KF5ipxWIQW6a4g8wnxhaQTMe0Am12rVr17333qvM7hvee++9l19++bHHHrvtttuCP40ePXrJkiUHDx7suuu+7c8ZWrZLDAaH+aCp9rFhYiJKZPvgiAoOyWy77DNiCBXKFDEknTXxKam7aJ+utQVisGjWIYZAl1566aJFi3bu3HnhwgXJn/V4iU7W6mm+3auCeTqyoaXQ76GjJfglvM8WeXR8rc6tkYwS0CJB0VqNjIw8//zzU6dOTdjTvHnzxo8fL22cM2fOhg0bPv/8c81d921/ztBy1i6dWSFIMEYMwjEQGrZz7iCZGNqW1qGYek5oqetbUGFiMKTsJzamYyR0l5QhlaXu89GKzZyKGAKNHj36/vvv37Zt2/nz531/1uOlGwH7f++c2bfP8sMrBEJ3011jCHbs91YBQBI7rwliF2t15MiRKVOmDAwMJKQWNWnSJMdx1q1bd+DAAb/46M+iKkMMpiQOxtaIIevEVW1iCIaqyAmKNBLVHaq59bYh7BaqKjQRU/eG2IacIvR+KnYgJgx3I6eualj4h5rrCt0l2GMnWbruoTlWQiXUwMDA/Pnzh4eH/V+133NJMdN8pBMp5+twb9NYY2j9Iu/C39St44Zuf8gm/8WtWbPm6quvLv4tgnIp/durGoJVI2p4UBYPgfac0DL7g3JonA1NNkk7Dh0OnVk+YW9eCCb8f+nY4+lDNkT3nR4Zur4FPUsMDbfWeZsarlv3vPirEnWn8576b4XQM4Q1V3FJJ1hODfWFjqOzR2EFqvXnuiO8kXW3vcYbziZGEMNFo4VegNjT/Kbbbv3ukflghqzLEDHICh1Poa2KbpUwFsWuCNQdx3GCxYvWgOf/TtLBEJJTN7DK5zf18ssvz5079/LLL+d6r6AsSvvm1h1xmAyWsVRDtTQox16VEFG21SeFqSQ6WAv7Cx8A8WsMScRA4ZE/hBJBpMhc5UXhPf2B0/Ut6FViUFcjiRjU9zcE7YTXRsXNIWgLD6rSMlP7LVUsH0WWXgNPtOc4dcULDPnVLz0tMUgb+3xFlM1s+6qEQqHBtKNWV0u4vaAz98ercxy1FgtUcCIdWvHLFtkk1arRaDz66KOTJk3yt8ddofjTn/6kbA39WVSZrkoEo2RkqFav6ScRQ8zc3m4nZuyPrr9lIQZpHS5oOYJDXnimSFj801RliUF9TSeBGJQrBYqbWjtqbxaLHgyPIfxUjKHyOxXJG6QIu0VUkT/WFrkqIa2PgRhsxSiOGC677DLDdz4m3nUgdGPFRQSxDfEIkq6kSb86dc9ruI7bEC4vKELE30xp4FaiuFodOXJkzZo1M2bMIKJ/+Zd/kSLt2bNH2Rr6syjrxCB1JuHcW/hdfYqZRAzROSLYm9+lVWO/GWJQj/whYghLOK5ADGqpb5fKSQyK8qrPb9oEq76VRbU1eY1BTQxq0gxtCixZrkpIG/t8tGIzl/HTlcqx1Kl3/hQexiRiiMMOGQVCxJBACJHfhV8Lup/r0KFDzzzzzC233CIm37Jli9KM/izKMjHErx8HWzSH6vTEoFr6MnFVQr6ZTToGY+Yqr1euSowYUrruIs2XXe9j6EYM4XWshuoz4P6JUqhB+e4ExRWu2PsYBBZWvctiG+K13s5Vu/jlqG7ySy3Vv9lsmnor2VSlzIsXL544ceLDDz/86quv6rRTLDFE/xQzbWutMbT/LSMxhPg6yxUKZYdX6vDhw08//bT/6AXtt9SYqtSfMygnMXTuPlAM1apBOQ8xxI39kQvZMcQQuQ0idB9DaOSPzGIxc1V03+nhWv9I0ZGyb9j7rETn7CZ7aurxAAAFu0lEQVT0prfPa1ISgyeu9sScT6kM6ptWBavybCyOBMXfQi1Lt71Iragv0sRKWeo+P79hM5fuKdGaxKD3H0lTeqqrEoFENM902uRlOnM6efKkvjlVywmqUn/O0HK2Lh10lprrhm8+V4zlipGzbUpJDF7c2C/NGNK1YyGSEMeV1hjqoU91KNpSvr6kXWsqw5GSoBJdlWCUgRuvClbqcRTEYNFcmm+iaihGJUnRqwPd/kObGFRrCtK150gWitwxqSmz42D5+wan2ToxlE6NmCc46SnbTB/8r3LpWl8gBhOKu2RVDsVf1YoViMGiuTTE0EcCMfRE5sp06cgN9SkG6IzEELpTOcOc0BKIAVIIxGDRDGLgF4ihJzKjS3v51hjyC8QAKQRisGgGMfALxNATmdGlrQvEACkEYrBoBjHwC8TQE5nRpa0LxAApBGKwaAYx8AvE0BOZ0aWtC8QAKQRisGgGMfALxNATmdGlrQvEACkEYrBoBjHwC8TQE5nRpa0LxAApBGKwaAYx8AvE0BOZ0aWtC8QAKQRisGgGMfALxNATmdGlrQvEACkEYrBoBjHwC8TQE5nRpa0LxAApBGKwaAYx8AvE0BOZ0aWtC8QAKQRisGgGMfALxNATmdGlrYuDGEYMCd2FTX6ppfr3+TftsslUZhwv+lJ2+BKqn/vzCLp0CWT2SCnZt11DWYU1BotmrDHwC2sMPZEZXdq6cFUCUgjEYNEMYuAXiKEnMqNLWxeIAVIIxGDRDGLgF4ihJzKjS1sXiAFSCMRg0Qxi4BeIoScyo0tbF4gBUgjEYNEMYuAXiKEnMqNLWxeIAVIIxGDRDGLgF4ihJzKjS1sXiAFSCMRg0Qxi4BeIoScyo0tbF4gBUgjEYNEMYuAXiKEnMqNLWxeIAVIIxGDRDGLgF4ihJzKjS1sXiAFSCMRg0Qxi4BeIoScyo0tbF4gBUgjEYNEMYuAXiKEnMqNLWxeIAVIIxGDRDGLgF4ihJzKjS1sXiAFSCMRg0Qxi4BeIoScyE1QOZX4HJYEYKiJlqft8tGIzgxj4ZXYcLH/f4DQbzDw4OMg/O0KSBgcHM7+Dkji+7Rpik1T/Pv+mXTaZ/bZrSF9Gyl6o+rk/cwqZeVTst10DMDkVBck+P79hM5vKjOMllQyeOZW/b3CakZnHXJnMxogh1V6NtIzMopCZx4zMPGZk5jEjM4+5MplBDAohM48ZmXnMyMxjRmYeMzLzmEEMukJmHjMy85iRmceMzDxmZOYxgxh0hcw8ZmTmMSMzjxmZeczIzGMGMegKmXnMyMxjRmYeMzLzmJGZxwxi0BUy85iRmceMzDxmZOYxIzOPGcSgK2TmMSMzjxmZeczIzGNGZh4ziEFXyMxjRmYeMzLzmJGZx4zMPGYQg66QmceMzDxmZOYxIzOPGZl5zCAGXSEzjxmZeczIzGNGZh4zMvOYQQy6QmYeMzLzmJGZx4zMPGZk5jGDGHSFzDxmZOYxIzOPGZl5zMjMYwYx6AqZeczIzGNGZh4zMvOYkZnHrCaGZrPZbDZHRkaa2kplTqXiYiAzTwxk5omBzDwxkJknBjLzxMifGWsMCiEzjxmZeczIzGNGZh4zMvOYlZlBDAohM48ZmXnMyMxjRmYeMzLzmEEMukJmHjMy85iRmceMzDxmZOYxgxh0hcw8ZmTmMSMzjxmZeczIzGMGMegKmXnMyMxjRmYeMzLzmJGZxwxi0BUy85iRmceMzDxmZOYxIzOPGcSgK2TmMSMzjxmZeczIzGNGZh4ziEFXyMxjRmYeMzLzmJGZx4zMPGYQg66QmceMzDxmZOYxIzOPGZl5zCAGXSEzjxmZeczIzGNGZh4zMvOYQQy6QmYeMzLzmJGZx4zMPGZk5jGDGHSFzDxmZOYxIzOPGZl5zMjMYwYx6AqZeczIzGNGZh4zMvOYkZnHDGLQFTLzmJGZx4zMPGZk5jEjM49Zmfn/A7Zec7Q1xK2YAAAAAElFTkSuQmCC" 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