From 779e9652b65b735fd0c3186ab29345d3e5caadce Mon Sep 17 00:00:00 2001 From: Fengda HUANG Date: Fri, 23 Oct 2015 22:27:02 +0800 Subject: [PATCH] update analtyics 2 --- chapters/04-analytics-02.md | 418 +++++++++++++++++++++--------------- img/main-events.png | Bin 0 -> 18949 bytes img/smtwtfs.png | Bin 0 -> 8528 bytes 3 files changed, 239 insertions(+), 179 deletions(-) create mode 100644 img/main-events.png create mode 100644 img/smtwtfs.png diff --git a/chapters/04-analytics-02.md b/chapters/04-analytics-02.md index 3fb2d69..5b56c85 100644 --- a/chapters/04-analytics-02.md +++ b/chapters/04-analytics-02.md @@ -3,25 +3,34 @@ 让我们分析之前的程序，然后再想办法做出优化。网上看到一篇文章[http://www.huyng.com/posts/python-performance-analysis/](http://www.huyng.com/posts/python-performance-analysis/)讲的就是分析这部分内容的。 -#time python分析# +##time python分析 + 分析程序的运行时间 - $time python handle.py +```bash +$time python handle.py +``` 结果便是，但是对于我们的分析没有一点意义 - real 0m43.411s - user 0m39.226s - sys 0m0.618s +``` + real 0m43.411s + user 0m39.226s + sys 0m0.618s +``` + +##line_profiler python -#line_profiler python# 这是 ##Mac OS X 10.9 line_profiler Install## - sudo ARCHFLAGS="-Wno-error=unused-command-line-argument-hard-error-in-future" easy_install line_profiler +```bash +sudo ARCHFLAGS="-Wno-error=unused-command-line-argument-hard-error-in-future" easy_install line_profiler +``` 然后在我们的``parse_data.py``的``handle_json``前面加上``@profile`` -

+
+```python
 @profile
 def handle_json(jsonfile):
     f = open(jsonfile, "r")
@@ -37,107 +46,127 @@ def handle_json(jsonfile):
 
     f.close()
     return datacount, dataarray
-
+``` + Line_profiler带了一个分析脚本``kernprof.py``，so - kernprof.py -l -v handle.py +```bash +kernprof.py -l -v handle.py +``` 我们便会得到下面的结果 +``` +Wrote profile results to handle.py.lprof +Timer unit: 1e-06 s - Wrote profile results to handle.py.lprof - Timer unit: 1e-06 s +File: parse_data.py +Function: handle_json at line 15 +Total time: 127.332 s - File: parse_data.py - Function: handle_json at line 15 - Total time: 127.332 s - - Line # Hits Time Per Hit % Time Line Contents - ============================================================== - 15 @profile - 16 def handle_json(jsonfile): - 17 19 636 33.5 0.0 f = open(jsonfile, "r") - 18 19 21 1.1 0.0 dataarray = [] - 19 19 16 0.8 0.0 datacount = 0 - 20 - 21 212373 730344 3.4 0.6 for line in open(jsonfile): - 22 212354 2826826 13.3 2.2 line = f.readline() - 23 212354 13848171 65.2 10.9 lin = json.loads(line) - 24 212354 109427317 515.3 85.9 date = dateutil.parser.parse(lin["created_at"]) - 25 212354 238112 1.1 0.2 datacount += 1 - 26 212354 260227 1.2 0.2 dataarray.append(date.minute) - 27 - 28 19 349 18.4 0.0 f.close() - 29 19 20 1.1 0.0 return datacount, dataarray +Line # Hits Time Per Hit % Time Line Contents +============================================================== + 15 @profile + 16 def handle_json(jsonfile): + 17 19 636 33.5 0.0 f = open(jsonfile, "r") + 18 19 21 1.1 0.0 dataarray = [] + 19 19 16 0.8 0.0 datacount = 0 + 20 + 21 212373 730344 3.4 0.6 for line in open(jsonfile): + 22 212354 2826826 13.3 2.2 line = f.readline() + 23 212354 13848171 65.2 10.9 lin = json.loads(line) + 24 212354 109427317 515.3 85.9 date = dateutil.parser.parse(lin["created_at"]) + 25 212354 238112 1.1 0.2 datacount += 1 + 26 212354 260227 1.2 0.2 dataarray.append(date.minute) + 27 + 28 19 349 18.4 0.0 f.close() + 29 19 20 1.1 0.0 return datacount, dataarray +``` 于是我们就发现我们的瓶颈就是从读取``created_at``，即创建时间。。。以及解析json，反而不是我们关心的IO，果然``readline``很强大。 -#memory_profiler python# -##memory_profiler install## +##memory_profiler python - $ pip install -U memory_profiler - $ pip install psutil +###memory_profiler install + +```bash +$ pip install -U memory_profiler +$ pip install psutil +``` + +###memory_profiler python -##memory_profiler python## 如上，我们只需要在``handle_json``前面加上``@profile`` - python -m memory_profiler handle.py +```bash +python -m memory_profiler handle.py +``` 于是 +``` +Filename: parse_data.py + +Line # Mem usage Increment Line Contents +================================================ + 13 39.930 MiB 0.000 MiB @profile + 14 def handle_json(jsonfile): + 15 39.930 MiB 0.000 MiB f = open(jsonfile, "r") + 16 39.930 MiB 0.000 MiB dataarray = [] + 17 39.930 MiB 0.000 MiB datacount = 0 + 18 + 19 40.055 MiB 0.125 MiB for line in open(jsonfile): + 20 40.055 MiB 0.000 MiB line = f.readline() + 21 40.066 MiB 0.012 MiB lin = json.loads(line) + 22 40.055 MiB -0.012 MiB date = dateutil.parser.parse(lin["created_at"]) + 23 40.055 MiB 0.000 MiB datacount += 1 + 24 40.055 MiB 0.000 MiB dataarray.append(date.minute) + 25 + 26 f.close() + 27 return datacount, dataarray +``` - Filename: parse_data.py - - Line # Mem usage Increment Line Contents - ================================================ - 13 39.930 MiB 0.000 MiB @profile - 14 def handle_json(jsonfile): - 15 39.930 MiB 0.000 MiB f = open(jsonfile, "r") - 16 39.930 MiB 0.000 MiB dataarray = [] - 17 39.930 MiB 0.000 MiB datacount = 0 - 18 - 19 40.055 MiB 0.125 MiB for line in open(jsonfile): - 20 40.055 MiB 0.000 MiB line = f.readline() - 21 40.066 MiB 0.012 MiB lin = json.loads(line) - 22 40.055 MiB -0.012 MiB date = dateutil.parser.parse(lin["created_at"]) - 23 40.055 MiB 0.000 MiB datacount += 1 - 24 40.055 MiB 0.000 MiB dataarray.append(date.minute) - 25 - 26 f.close() - 27 return datacount, dataarray +##objgraph python +###objgraph install -#objgraph python# - -##objgraph install## - - pip install objgraph +```bash +pip install objgraph +``` 我们需要调用他 - import pdb; +```python +import pdb; +``` 以及在需要调度的地方加上 - pdb.set_trace() +```python +pdb.set_trace() +``` 接着会进入``command``模式 - (pdb) import objgraph - (pdb) objgraph.show_most_common_types() +```python +(pdb) import objgraph +(pdb) objgraph.show_most_common_types() +``` 然后我们可以找到。。 - function 8259 - dict 2137 - tuple 1949 - wrapper_descriptor 1625 - list 1586 - weakref 1145 - builtin_function_or_method 1117 - method_descriptor 948 - getset_descriptor 708 - type 705 +``` +function 8259 +dict 2137 +tuple 1949 +wrapper_descriptor 1625 +list 1586 +weakref 1145 +builtin_function_or_method 1117 +method_descriptor 948 +getset_descriptor 708 +type 705 +``` 也可以用他生成图形，貌似这里是用``dot``生成的，加上``python-xdot`` @@ -145,17 +174,20 @@ Line_profiler带了一个分析脚本``kernprof.py``，so 如果我们每次都要花同样的时间去做一件事，去扫那些数据的话，那么这是最好的打发时间的方法。 -##python SQLite3 查询数据## +##python SQLite3 查询数据 + 我们创建了一个名为``userdata.db``的数据库文件，然后创建了一个表，里面有owner,language,eventtype,name url - def init_db(): - conn = sqlite3.connect('userdata.db') - c = conn.cursor() - c.execute('''CREATE TABLE userinfo (owner text, language text, eventtype text, name text, url text)''') +```python +def init_db(): + conn = sqlite3.connect('userdata.db') + c = conn.cursor() + c.execute('''CREATE TABLE userinfo (owner text, language text, eventtype text, name text, url text)''') +``` 接着我们就可以查询数据，这里从结果讲起。 -

+```python
 def get_count(username):
     count = 0
     userinfo = []
@@ -165,11 +197,11 @@ def get_count(username):
         userinfo.append(zero)
 
     return count, userinfo
-
-
+``` 当我查询``gmszone``的时候，也就是我自己就会有如下的结果 -

+
+```bash
 (u'gmszone', u'ForkEvent', u'RESUME', u'TeX', u'https://github.com/gmszone/RESUME')
 (u'gmszone', u'WatchEvent', u'iot-dashboard', u'JavaScript', u'https://github.com/gmszone/iot-dashboard')
 (u'gmszone', u'PushEvent', u'wechat-wordpress', u'Ruby', u'https://github.com/gmszone/wechat-wordpress')
@@ -180,43 +212,53 @@ def get_count(username):
 (u'gmszone', u'PushEvent', u'iot-doc', u'TeX', u'https://github.com/gmszone/iot-doc')
 (u'gmszone', u'PushEvent', u'iot-doc', u'TeX', u'https://github.com/gmszone/iot-doc')
 109
-
+```` 一共有109个事件，有``Watch``,``Create``,``Push``,``Fork``还有其他的， 项目主要有``iot``,``RESUME``,``iot-dashboard``,``wechat-wordpress``, 接着就是语言了，``Tex``,``Javascript``,``Ruby``,接着就是项目的url了。 值得注意的是。 -

+
+```bash
 -rw-r--r--   1 fdhuang staff 905M Apr 12 14:59 userdata.db
-
+``` + 这个数据库文件有**905M**，不过查询结果相当让人满意，至少相对于原来的结果来说。 -##Python SQLite3## +##Python SQLite3 Python自带了对SQLite3的支持，然而我们还需要安装SQLite3 - brew install sqlite3 +```bash +brew install sqlite3 +``` 或者是 - - sudo port install sqlite3 + +```bash +sudo port install sqlite3 +``` 或者是Ubuntu的 - sudo apt-get install sqlite3 +```bash +sudo apt-get install sqlite3 +``` openSUSE自然就是 - sudo zypper install sqlite3 +```bash +sudo zypper install sqlite3 +``` 不过，用yast2也很不错，不是么。。 -##Pythont Github Sqlite3数据导入## +##Pythont Github Sqlite3数据导入 需要注意的是这里是需要python2.7，起源于对gzip的上下文管理器的支持问题 -

+```python
 def handle_gzip_file(filename):
     userinfo = []
     with gzip.GzipFile(filename) as f:
@@ -264,7 +306,7 @@ def build_db_with_gzip():
 
     conn.commit()
     c.close()
-
+``` ``executemany``可以插入多条数据，对于我们的数据来说，一小时的文件大概有五六千个会符合我们上面的安装，也就是有``actor``又有``type``才是我们需要记录的数据，我们只需要统计用户的那些事件，而非全部的事件。 @@ -276,7 +318,9 @@ def build_db_with_gzip(): 首先是正规匹配 - date_re = re.compile(r"([0-9]{4})-([0-9]{2})-([0-9]{2})-([0-9]+)\.json.gz") +```python +date_re = re.compile(r"([0-9]{4})-([0-9]{2})-([0-9]{2})-([0-9]+)\.json.gz") +``` 不过主要的还是在于``glob.glob`` @@ -290,7 +334,7 @@ def build_db_with_gzip(): 更好的方案？ -###redis### +###redis 结合了前面两篇我们终于可以成功地读取出用户数据、处理，再接着可以找相近的用户。 @@ -298,30 +342,36 @@ def build_db_with_gzip(): 查询用户事件总数 - import redis - r = redis.StrictRedis(host='localhost', port=6379, db=0) - pipe = pipe = r.pipeline() - pipe.zscore('osrc:user',"gmszone") - pipe.execute() +```python +import redis +r = redis.StrictRedis(host='localhost', port=6379, db=0) +pipe = pipe = r.pipeline() +pipe.zscore('osrc:user',"gmszone") +pipe.execute() +``` 系统返回了``227.0``,试试别人。 - >>> pipe.zscore('osrc:user',"dfm") - - >>> pipe.execute() - [425.0] - >>> +```bash +>>> pipe.zscore('osrc:user',"dfm") + +>>> pipe.execute() +[425.0] +>>> +``` 看看主要是在哪一天提交的 - >>> pipe.hgetall('osrc:user:gmszone:day') - - >>> pipe.execute() - [{'1': '51', '0': '41', '3': '17', '2': '34', '5': '28', '4': '22', '6': '34'}] +```python +>>> pipe.hgetall('osrc:user:gmszone:day') + +>>> pipe.execute() +[{'1': '51', '0': '41', '3': '17', '2': '34', '5': '28', '4': '22', '6': '34'}] +``` 结果大致如下图所示: -![SMTWTFS][1] +![SMTWTFS](./img/smtwtfs.png) 看看主要的事件是？ @@ -331,17 +381,17 @@ def build_db_with_gzip(): [[('PushEvent', 154.0), ('CreateEvent', 41.0), ('WatchEvent', 18.0), ('GollumEvent', 8.0), ('MemberEvent', 3.0), ('ForkEvent', 2.0), ('ReleaseEvent', 1.0)]] >>> -![Main Event][2] +![Main Event](./img/main-events.png) 蓝色的就是push事件，黄色的是create等等。 到这里我们算是知道了OSRC的数据库部分是如何工作的。 -##Python redis 查询 +###Python redis 查询 主要代码如下所示 -

+```python
 def get_vector(user, pipe=None):
 
     r = redis.StrictRedis(host='localhost', port=6379, db=0)
@@ -364,19 +414,20 @@ def get_vector(user, pipe=None):
 
     if no_pipe:
         return pipe.execute()
-
+``` 结果在上一篇中显示出来了，也就是 - [227.0, {'1': '51', '0': '41', '3': '17', '2': '34', '5': '28', '4': '22', '6': '34'}, [('PushEvent', 154.0), ('CreateEvent', 41.0), ('WatchEvent', 18.0), ('GollumEvent', 8.0), ('MemberEvent', 3.0), ('ForkEvent', 2.0), ('ReleaseEvent', 1.0)], 0, 0, 0, 11, [('CSS', 74.0), ('JavaScript', 60.0), ('Ruby', 12.0), ('TeX', 6.0), ('Python', 6.0), ('Java', 5.0), ('C++', 5.0), ('Assembly', 5.0), ('C', 3.0), ('Emacs Lisp', 2.0), ('Arduino', 2.0)]] +``` +[227.0, {'1': '51', '0': '41', '3': '17', '2': '34', '5': '28', '4': '22', '6': '34'}, [('PushEvent', 154.0), ('CreateEvent', 41.0), ('WatchEvent', 18.0), ('GollumEvent', 8.0), ('MemberEvent', 3.0), ('ForkEvent', 2.0), ('ReleaseEvent', 1.0)], 0, 0, 0, 11, [('CSS', 74.0), ('JavaScript', 60.0), ('Ruby', 12.0), ('TeX', 6.0), ('Python', 6.0), ('Java', 5.0), ('C++', 5.0), ('Assembly', 5.0), ('C', 3.0), ('Emacs Lisp', 2.0), ('Arduino', 2.0)]] +``` 有意思的是在这里生成了和自己相近的人 - ['alesdokshanin', 'hjiawei', 'andrewreedy', 'christj6', '1995eaton'] +``` +['alesdokshanin', 'hjiawei', 'andrewreedy', 'christj6', '1995eaton'] +``` - [1]: https://www.phodal.com/static/media/uploads/screen_shot_2014-04-15_at_8.11.14_pm.png - [2]: https://www.phodal.com/static/media/uploads/screen_shot_2014-04-15_at_8.14.52_pm.png - osrc最有意思的一部分莫过于flann，当然说的也是系统后台的设计的一个很关键及有意思的部分。 ##Python Github @@ -386,20 +437,24 @@ osrc最有意思的一部分莫过于flann，当然说的也是系统后台的 换句话说，我们需要一些样本来当作我们的分析资料，这里东西用到的就是我们之前的。 - [227.0, {'1': '51', '0': '41', '3': '17', '2': '34', '5': '28', '4': '22', '6': '34'}, [('PushEvent', 154.0), ('CreateEvent', 41.0), ('WatchEvent', 18.0), ('GollumEvent', 8.0), ('MemberEvent', 3.0), ('ForkEvent', 2.0), ('ReleaseEvent', 1.0)], 0, 0, 0, 11, [('CSS', 74.0), ('JavaScript', 60.0), ('Ruby', 12.0), ('TeX', 6.0), ('Python', 6.0), ('Java', 5.0), ('C++', 5.0), ('Assembly', 5.0), ('C', 3.0), ('Emacs Lisp', 2.0), ('Arduino', 2.0)]] +``` +[227.0, {'1': '51', '0': '41', '3': '17', '2': '34', '5': '28', '4': '22', '6': '34'}, [('PushEvent', 154.0), ('CreateEvent', 41.0), ('WatchEvent', 18.0), ('GollumEvent', 8.0), ('MemberEvent', 3.0), ('ForkEvent', 2.0), ('ReleaseEvent', 1.0)], 0, 0, 0, 11, [('CSS', 74.0), ('JavaScript', 60.0), ('Ruby', 12.0), ('TeX', 6.0), ('Python', 6.0), ('Java', 5.0), ('C++', 5.0), ('Assembly', 5.0), ('C', 3.0), ('Emacs Lisp', 2.0), ('Arduino', 2.0)]] +``` 在代码中是构建了一个points.h5的文件来分析每个用户的points，之后再记录到hdf5文件中。 - [ 0.00438596 0.18061674 0.2246696 0.14977974 0.07488987 0.0969163 - 0.12334802 0.14977974 0. 0.18061674 0. 0. 0. - 0.00881057 0. 0. 0.03524229 0. 0. - 0.01321586 0. 0. 0. 0.6784141 0. - 0.07929515 0.00440529 1. 1. 1. 0.08333333 - 0.26431718 0.02202643 0.05286344 0.02643172 0. 0.01321586 - 0.02202643 0. 0. 0. 0. 0. 0. - 0. 0. 0.00881057 0. 0. 0. 0. - 0. 0. 0. 0. 0. 0. 0. - 0. 0. 0. 0. 0.00881057] +``` +[ 0.00438596 0.18061674 0.2246696 0.14977974 0.07488987 0.0969163 + 0.12334802 0.14977974 0. 0.18061674 0. 0. 0. + 0.00881057 0. 0. 0.03524229 0. 0. + 0.01321586 0. 0. 0. 0.6784141 0. + 0.07929515 0.00440529 1. 1. 1. 0.08333333 + 0.26431718 0.02202643 0.05286344 0.02643172 0. 0.01321586 + 0.02202643 0. 0. 0. 0. 0. 0. + 0. 0. 0.00881057 0. 0. 0. 0. + 0. 0. 0. 0. 0. 0. 0. + 0. 0. 0. 0. 0.00881057] +``` 这里分析到用户的大部分行为，再找到与其行为相近的用户，主要的行为有下面这些: @@ -410,62 +465,67 @@ osrc最有意思的一部分莫过于flann，当然说的也是系统后台的 osrc中用于解析的代码 +```python +def parse_vector(results): + points = np.zeros(nvector) + total = int(results[0]) - def parse_vector(results): - points = np.zeros(nvector) - total = int(results[0]) + points[0] = 1.0 / (total + 1) - points[0] = 1.0 / (total + 1) + # Week means. + for k, v in results[1].iteritems(): + points[1 + int(k)] = float(v) / total - # Week means. - for k, v in results[1].iteritems(): - points[1 + int(k)] = float(v) / total + # Event types. + n = 8 + for k, v in results[2]: + points[n + evttypes.index(k)] = float(v) / total - # Event types. - n = 8 - for k, v in results[2]: - points[n + evttypes.index(k)] = float(v) / total + # Number of contributions, connections and languages. + n += nevts + points[n] = 1.0 / (float(results[3]) + 1) + points[n + 1] = 1.0 / (float(results[4]) + 1) + points[n + 2] = 1.0 / (float(results[5]) + 1) + points[n + 3] = 1.0 / (float(results[6]) + 1) - # Number of contributions, connections and languages. - n += nevts - points[n] = 1.0 / (float(results[3]) + 1) - points[n + 1] = 1.0 / (float(results[4]) + 1) - points[n + 2] = 1.0 / (float(results[5]) + 1) - points[n + 3] = 1.0 / (float(results[6]) + 1) + # Top languages. + n += 4 + for k, v in results[7]: + if k in langs: + points[n + langs.index(k)] = float(v) / total + else: + # Unknown language. + points[-1] = float(v) / total - # Top languages. - n += 4 - for k, v in results[7]: - if k in langs: - points[n + langs.index(k)] = float(v) / total - else: - # Unknown language. - points[-1] = float(v) / total - - return points + return points +``` 这样也就返回我们需要的点数，然后我们可以用``get_points``来获取这些 - def get_points(usernames): - r = redis.StrictRedis(host='localhost', port=6379, db=0) - pipe = r.pipeline() +```python +def get_points(usernames): + r = redis.StrictRedis(host='localhost', port=6379, db=0) + pipe = r.pipeline() - results = get_vector(usernames) - points = np.zeros([len(usernames), nvector]) - points = parse_vector(results) - return points + results = get_vector(usernames) + points = np.zeros([len(usernames), nvector]) + points = parse_vector(results) + return points +``` 就会得到我们的相应的数据，接着找找和自己邻近的，看看结果。 - [ 0.01298701 0.19736842 0. 0.30263158 0.21052632 0.19736842 - 0. 0.09210526 0. 0.22368421 0.01315789 0. 0. - 0. 0. 0. 0.01315789 0. 0. - 0.01315789 0. 0. 0. 0.73684211 0. 0. - 0. 1. 1. 1. 0.2 0.42105263 - 0.09210526 0. 0. 0. 0. 0.23684211 - 0. 0. 0.03947368 0. 0. 0. 0. - 0. 0. 0. 0. 0. 0. 0. - 0. 0. 0. 0. 0. 0. 0. - 0. 0. 0. 0. ] +``` +[ 0.01298701 0.19736842 0. 0.30263158 0.21052632 0.19736842 + 0. 0.09210526 0. 0.22368421 0.01315789 0. 0. + 0. 0. 0. 0.01315789 0. 0. + 0.01315789 0. 0. 0. 0.73684211 0. 0. + 0. 1. 1. 1. 0.2 0.42105263 + 0.09210526 0. 0. 0. 0. 0.23684211 + 0. 0. 0.03947368 0. 0. 0. 0. + 0. 0. 0. 0. 0. 0. 0. + 0. 0. 0. 0. 0. 0. 0. + 0. 0. 0. 0. ] +``` 真看不出来两者有什么相似的地方 。。。。 diff --git a/img/main-events.png b/img/main-events.png new file mode 100644 index 0000000000000000000000000000000000000000..37ba331a608468395f6f7de1e93bde1df4faf0db GIT binary patch literal 18949 zcmeFZbyU<*_czLn1Cr99h%)5RDd5oEAR(QSB0V4>4U&>W!vKn)(%mWDD&5`P9rvrx z?|t5P-Mj98_s`2(v)0TxXP?+-?|t^(=W_^CRguNRrocu+L&KAolTt@R15^C{V1j{? z&1q*RG&B&0wWOq~yrd*l72#lMZEJys#z<;rV#4j{;)JjuGBN2MU}3^WxT%MRMyQ)K zx3v6h8G!bi^qZt78yGA+CRk`ggLqte53%zX|Bg>OYAfLZ^N3-~oD9{7v@jpoQKgThq}T3dg?BFmuxg+B~+%6#y1v5(OegF3i` zv9{s|Zm*d+x1|ZRT{f9ZHo*U!5dL;v>s#?;}xi!cq%-+})7_n&!MxLg14k?fuS zXIa1kIsVpgaItf8{I_p_ROoN1fU32-g{_X1wVj2%Gcbk-FV9P%f9?MtHUE3W|ASKZ z|CRFpsQDjCA&$Qb_&*l(A8!3C1=uBmEyVF(){9`@K@I`I7ekYm5`XOu+D*rDf3EGZ zFREK#sawXX7)3i>O2i>A7iw0PHlqqXAybgd)K>@R81xRESxf4FHuy|F^s(v$Y>m@M zz-WL8NoBBhl<0XBwQL);TTr#r{b@yctVyqoZ~pw4@$0L30iOAS^_!awdCNf*yl-Fl 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