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<!DOCTYPE html>
<html lang="cn">
<head>
<meta charset="utf-8" />
<title>python自动化测试人工智能</title>
<link rel="stylesheet" href="/theme/css/main.css" />
</head>
<body id="index" class="home">
<header id="banner" class="body">
<h1><a href="/">python自动化测试人工智能 </a></h1>
<nav><ul>
<li><a href="/category/ba-zi.html">八字</a></li>
<li><a href="/category/ce-shi.html">测试</a></li>
<li><a href="/category/ce-shi-kuang-jia.html">测试框架</a></li>
<li><a href="/category/common.html">common</a></li>
<li><a href="/category/da-shu-ju.html">大数据</a></li>
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<section id="content" class="body">
<ol id="posts-list" class="hfeed" start="9">
<li><article class="hentry">
<header>
<h1><a href="/python3_lib_multiprocessing.html" rel="bookmark"
title="Permalink to python库介绍-multiprocessing：多进程">python库介绍-multiprocessing：多进程</a></h1>
</header>
<div class="entry-content">
<footer class="post-info">
<abbr class="published" title="2018-03-21T19:20:00+08:00">
Published: 三 21 三月 2018
</abbr>
<address class="vcard author">
By <a class="url fn" href="/author/andrew.html">andrew</a>
</address>
<p>In <a href="/category/python.html">python</a>.</p>
</footer><!-- /.post-info --> <h2 id="_1">简介</h2>
<p>进程是运行的程序，每个进程有自己的系统状态，包含了内存、打开文件列表、程序计数器（跟踪执行的指令）、存储函数本地调用变量的堆栈。</p>
<p>使用os或subprocess可以创建新进程，比如：os.fork(), subprocess.Popen()。子进程和父进程是相互独立执行的。</p>
<p>interprocess communication (IPC)进程间的通信: 最常见的形式是基于消息传递（message passing）。message是原始字节的缓存，通过I/O channel比如网络socket和管道，使用原语比如send() and recv()来发送接收消息。次常用的有内存映射区：memory-mapped regions，见mmap模块，实际上是共享内存。</p>
<p>线程有自己的控制流和执行堆栈，但是共享系统资源和数据。</p>
<p>并发的难点：同步和数据共享。解决的方法一般是使用互斥锁。</p>
<div class="highlight"><pre><span></span><span class="n">write_lock</span> <span class="o">=</span> <span class="n">Lock</span><span class="p">()</span>
<span class="o">...</span>
<span class="c1"># Critical section where writing occurs</span>
<span class="n">write_lock</span><span class="o">.</span><span class="n">acquire</span><span class="p">()</span>
<span class="n">f …</span></pre></div>
<a class="readmore" href="/python3_lib_multiprocessing.html">read more</a>
</div><!-- /.entry-content -->
</article></li>
<li><article class="hentry">
<header>
<h1><a href="/python3_lib_pyjniu.html" rel="bookmark"
title="Permalink to python库介绍-pyjnius：访问java类">python库介绍-pyjnius：访问java类</a></h1>
</header>
<div class="entry-content">
<footer class="post-info">
<abbr class="published" title="2018-03-18T19:20:00+08:00">
Published: 日 18 三月 2018
</abbr>
<address class="vcard author">
By <a class="url fn" href="/author/andrew.html">andrew</a>
</address>
<p>In <a href="/category/python.html">python</a>.</p>
</footer><!-- /.post-info --> <h2 id="_1">简介</h2>
<p>Pyjnius是一个用于访问Java类的Python库。</p>
<p>适用场景：极个别的加密算法等内容，用python不方便实现或者实现较耗时，可基于Pyjnius把java类当做python库使用。</p>
<h3 id="_2">参考资料：</h3>
<hr>
<ul>
<li>
<p>python中文库参考项目： https://bitbucket.org/china-testing/python-chinese-library</p>
</li>
<li>
<p>python中文库参考项目文档：https://bitbucket.org/china-testing/python-chinese-library/wiki/browse/</p>
</li>
<li>
<p>技术支持 (可以加钉钉pythontesting邀请加入) qq群：144081101 591302926 567351477</p>
</li>
<li>
<p>道家技术-手相手诊看相中医等钉钉群21734177 qq群：391441566 184175668 338228106 看手相、面相、舌相、抽签、体质识别。服务费50元每人次起。请联系钉钉或者微信pythontesting</p>
</li>
</ul>
<p><a href="https://china-testing.github.io/testing_training.html">接口自动化性能测试数据分析人工智能从业专家一对一线上培训大纲</a></p>
<ul>
<li>
<p>文档：http://pyjnius.readthedocs.io/en/latest/installation.html</p>
</li>
<li>
<p>下载地址 …</p></li></ul>
<a class="readmore" href="/python3_lib_pyjniu.html">read more</a>
</div><!-- /.entry-content -->
</article></li>
<li><article class="hentry">
<header>
<h1><a href="/python3_lib_face_recognition.html" rel="bookmark"
title="Permalink to python库介绍-face_recognition 人脸识别">python库介绍-face_recognition 人脸识别</a></h1>
</header>
<div class="entry-content">
<footer class="post-info">
<abbr class="published" title="2018-03-12T19:20:00+08:00">
Published: 一 12 三月 2018
</abbr>
<address class="vcard author">
By <a class="url fn" href="/author/andrew.html">andrew</a>
</address>
<p>In <a href="/category/python.html">python</a>.</p>
</footer><!-- /.post-info --> <h3 id="_1">简介</h3>
<p>face_recognition使用世界上最简单的人脸识别工具，在Python或命令行中识别和操作人脸。</p>
<p>使用dlib最先进的人脸识别技术构建而成，并具有深度学习功能。 该模型在<a href="http://vis-www.cs.umass.edu/lfw/">Labeled Faces in the Wild</a>基准中的准确率为99.38％。</p>
<p>另外还提供了face_recognition命令行工具！</p>
<h3 id="_2">快速入门</h3>
<p>本节我们基于ubuntu16.04,python3，使用如下图片：</p>
<p><img alt="image.png" src="https://upload-images.jianshu.io/upload_images/10819934-625527be2687bd65.png?imageMogr2/auto-orient/strip%7CimageView2/2/w/1240"></p>
<ul>
<li>快速入门</li>
</ul>
<p>face_recognition</p>
<div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">face_recognition</span>
<span class="n">image</span> <span class="o">=</span> <span class="n">face_recognition</span><span class="o">.</span><span class="n">load_image_file</span><span class="p">(</span><span class="s2">"test0.jpg"</span><span class="p">)</span>
<span class="n">face_locations</span> <span class="o">=</span> <span class="n">face_recognition</span><span class="o">.</span><span class="n">face_locations</span><span class="p">(</span><span class="n">image</span><span class="p">,</span><span class="n">model</span><span class="o">=</span><span class="s2">"cnn"</span><span class="p">)</span>
<span class="k">print</span><span class="p">(</span><span class="n">face_locations</span><span class="p">)</span>
</pre></div>
<p>执行结果：</p>
<div class="highlight"><pre><span></span><span class="err">$</span> <span class="n">python3</span> <span class="n">quick</span><span class="o">.</span><span class="n">py</span>
<span class="p">[(</span><span class="mi">203</span><span class="p">,</span> <span class="mi">391</span><span class="p">,</span> <span class="mi">447</span><span class="p">,</span> <span class="mi">147</span><span class="p">)]</span>
</pre></div>
<p>model选择模型，默认为hog，该模式很多图片是无法识别的，为此一般用采用更精确但是速度更慢的cnn模型。</p>
<ul>
<li>显示图片：</li>
</ul>
<p>quick2 …</p>
<a class="readmore" href="/python3_lib_face_recognition.html">read more</a>
</div><!-- /.entry-content -->
</article></li>
<li><article class="hentry">
<header>
<h1><a href="/python3_lib_grp.html" rel="bookmark"
title="Permalink to python库介绍-grp：group数据库">python库介绍-grp：group数据库</a></h1>
</header>
<div class="entry-content">
<footer class="post-info">
<abbr class="published" title="2018-02-28T19:20:00+08:00">
Published: 三 28 二月 2018
</abbr>
<address class="vcard author">
By <a class="url fn" href="/author/andrew.html">andrew</a>
</address>
<p>In <a href="/category/python.html">python</a>.</p>
</footer><!-- /.post-info --> <h2 id="_1">简介</h2>
<p>grp可以访问Unix组数据库。 </p>
<p>组数据库项由结构体group(类似元组)表示，参见pwd.h：</p>
<table>
<thead>
<tr>
<th align="center">序号</th>
<th align="center">属性</th>
<th align="center">含义</th>
</tr>
</thead>
<tbody>
<tr>
<td align="center">0</td>
<td align="center">gr_name</td>
<td align="center">the name of the group</td>
</tr>
<tr>
<td align="center">1</td>
<td align="center">gr_passwd</td>
<td align="center">the (encrypted) group password; often empty</td>
</tr>
<tr>
<td align="center">2</td>
<td align="center">gr_gid</td>
<td align="center">the numerical group ID</td>
</tr>
<tr>
<td align="center">3</td>
<td align="center">gr_mem</td>
<td align="center">all the group member’s user names</td>
</tr>
</tbody>
</table>
<p>gid整型，name和password为字符串，gr_mem为字符串列表。</p>
<h2 id="_2">方法</h2>
<ul>
<li>
<p>grp.getgrgid(gid)</p>
<p>Return the group …</p></li></ul>
<a class="readmore" href="/python3_lib_grp.html">read more</a>
</div><!-- /.entry-content -->
</article></li>
<li><article class="hentry">
<header>
<h1><a href="/python3_lib_pwd.html" rel="bookmark"
title="Permalink to python库介绍-pwd：密码数据库">python库介绍-pwd：密码数据库</a></h1>
</header>
<div class="entry-content">
<footer class="post-info">
<abbr class="published" title="2018-02-22T19:20:00+08:00">
Published: 四 22 二月 2018
</abbr>
<address class="vcard author">
By <a class="url fn" href="/author/andrew.html">andrew</a>
</address>
<p>In <a href="/category/python.html">python</a>.</p>
</footer><!-- /.post-info --> <h2 id="_1">简介</h2>
<p>pwd可以访问Unix用户帐户和密码数据库。 </p>
<p>密码数据库项由结构体passwd(类似元组)表示，参见pwd.h：</p>
<table>
<thead>
<tr>
<th align="center">属性</th>
<th align="center">含义</th>
</tr>
</thead>
<tbody>
<tr>
<td align="center">pw_name</td>
<td align="center">Login name</td>
</tr>
<tr>
<td align="center">pw_passwd</td>
<td align="center">Optional encrypted password</td>
</tr>
<tr>
<td align="center">pw_uid</td>
<td align="center">Numerical user ID</td>
</tr>
<tr>
<td align="center">pw_gid</td>
<td align="center">Numerical group ID</td>
</tr>
<tr>
<td align="center">pw_gecos</td>
<td align="center">User name or comment field</td>
</tr>
<tr>
<td align="center">pw_dir</td>
<td align="center">User home directory</td>
</tr>
<tr>
<td align="center">pw_shell</td>
<td align="center">User command interpreter</td>
</tr>
</tbody>
</table>
<p>除了pw_uid和pw_gid是整数外，其他都是字符串。</p>
<h2 id="pwd">pwd方法</h2>
<ul>
<li>pwd.getpwuid(uid): Return the password database entry for …</li></ul>
<a class="readmore" href="/python3_lib_pwd.html">read more</a>
</div><!-- /.entry-content -->
</article></li>
<li><article class="hentry">
<header>
<h1><a href="/python3_lib_spwd.html" rel="bookmark"
title="Permalink to python库介绍-spwd：shadow密码数据库">python库介绍-spwd：shadow密码数据库</a></h1>
</header>
<div class="entry-content">
<footer class="post-info">
<abbr class="published" title="2018-02-12T19:20:00+08:00">
Published: 一 12 二月 2018
</abbr>
<address class="vcard author">
By <a class="url fn" href="/author/andrew.html">andrew</a>
</address>
<p>In <a href="/category/python.html">python</a>.</p>
</footer><!-- /.post-info --> <h2 id="_1">简介</h2>
<p>pwd可以访问Unix shadow密码数据库。 2.5新增。通常需要为root用户。</p>
<p>密码数据库项由结构体spwd(类似元组)表示，参见shadow.h：</p>
<table>
<thead>
<tr>
<th align="center">序号</th>
<th align="center">属性</th>
<th align="center">含义</th>
</tr>
</thead>
<tbody>
<tr>
<td align="center">0</td>
<td align="center">sp_nam</td>
<td align="center">Login name</td>
</tr>
<tr>
<td align="center">1</td>
<td align="center">sp_pwd</td>
<td align="center">Encrypted password</td>
</tr>
<tr>
<td align="center">2</td>
<td align="center">sp_lstchg</td>
<td align="center">Date of last change</td>
</tr>
<tr>
<td align="center">3</td>
<td align="center">sp_min</td>
<td align="center">Minimal number of days between changes</td>
</tr>
<tr>
<td align="center">4</td>
<td align="center">sp_max</td>
<td align="center">Maximum number of days between changes</td>
</tr>
<tr>
<td align="center">5</td>
<td align="center">sp_warn</td>
<td align="center">Number of days before …</td></tr></tbody></table>
<a class="readmore" href="/python3_lib_spwd.html">read more</a>
</div><!-- /.entry-content -->
</article></li>
<li><article class="hentry">
<header>
<h1><a href="/python3_lib_pathlib.html" rel="bookmark"
title="Permalink to python库介绍-pathlib: 文件系统对象">python库介绍-pathlib: 文件系统对象</a></h1>
</header>
<div class="entry-content">
<footer class="post-info">
<abbr class="published" title="2018-02-11T19:40:00+08:00">
Published: 日 11 二月 2018
</abbr>
<address class="vcard author">
By <a class="url fn" href="/author/andrew.html">andrew</a>
</address>
<p>In <a href="/category/python.html">python</a>.</p>
</footer><!-- /.post-info --> <p>目的：使用面向对象的API解析，构建，测试和处理文件名和路径，而不是底层的字符串操作。</p>
<h2 id="path">Path表示</h2>
<p>pathlib包含用于管理使用POSIX标准或Microsoft Windows语法格式化的文件系统路径的类。它包括所谓的“pure”类，它们对字符串进行操作，但不与实际的文件系统进行交互;“concrete”类将API扩展为包含反映或修改本地文件系统数据的操作。</p>
<p>pure类PurePosixPath和PureWindowsPath可以在任何操作系统上实例化和使用，因为它们只用于名称。要实例化正确的类来处理真实的文件系统，请使用Path来获取PosixPath或WindowsPath，具体取决于平台。</p>
<ul>
<li>
<p>技术支持 (可以加钉钉pythontesting邀请加入) qq群：144081101 591302926 567351477</p>
</li>
<li>
<p>道家技术-手相手诊看相中医等钉钉群21734177 qq群：391441566 184175668 338228106 看手相、面相、舌相、抽签、体质识别。服务费50元每人次起。请联系钉钉或者微信pythontesting</p>
</li>
</ul>
<p>[接口自动化性能测试数据分析人工智能从业专家一对一线上培训大纲]</p>
<h2 id="path_1">构建Path</h2>
<p>要实例化路径，字符串作为参数。Path对象的字符串表示是该字符串。要创建引用相对于现有路径的值的新路径，请使用/运算符来扩展路径。运算符的参数可以是字符串或其他路径对象。</p>
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<h1><a href="/python3_lib_enum.html" rel="bookmark"
title="Permalink to python库介绍-enum: 枚举">python库介绍-enum: 枚举</a></h1>
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<abbr class="published" title="2018-02-11T19:20:00+08:00">
Published: 日 11 二月 2018
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By <a class="url fn" href="/author/andrew.html">andrew</a>
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<p>In <a href="/category/python.html">python</a>.</p>
</footer><!-- /.post-info --> <h2 id="_1">简介</h2>
<p>枚举是常量的集合，支持根据id比较和迭代等功能。</p>
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<p>python官方文档：https://docs.python.org/3/library/enum.html</p>
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<p>pymotw文档：https://pymotw.com/3/enum/index.html</p>
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<p>完稿日期：2016.08.22</p>
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</ul>
<h2 id="_2">创建枚举</h2>
<p>继承Enum类，添加类属性即可创建枚举。</p>
<p>enum_create.py</p>
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title="Permalink to python库介绍-telnetlib：Telnet客户端">python库介绍-telnetlib：Telnet客户端</a></h1>
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<abbr class="published" title="2018-02-11T19:20:00+08:00">
Published: 日 11 二月 2018
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By <a class="url fn" href="/author/andrew.html">andrew</a>
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<p>In <a href="/category/python.html">python</a>.</p>
</footer><!-- /.post-info --> <ul>
<li><a href="https://china-testing.github.io/practices.html">python测试开发项目实战-目录</a></li>
<li><a href="https://china-testing.github.io/python_books.html">python工具书籍下载-持续更新</a></li>
<li><a href="https://china-testing.github.io/python3_quick.html">python 3.7极速入门教程 - 目录</a></li>
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<h2 id="_1">简介</h2>
<p>telnetlib模块提供的Telnet类实现了Telnet协议(参见rfc854)。它为协议字符和telnet选项提供符号常量，符号常量来源于arpa/telnet.h，去掉了前缀TELOPT_。</p>
<p>telnet命令有： IAC, DONT, DO, WONT, WILL, SE (Subnegotiation End), NOP (No Operation), DM (Data Mark), BRK (Break), IP (Interrupt process), AO (Abort output), AYT (Are You There), EC (Erase Character), EL (Erase Line), GA (Go Ahead …</p>
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<h1><a href="/python_pandas_excel.html" rel="bookmark"
title="Permalink to 使用pandas处理excel">使用pandas处理excel</a></h1>
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<abbr class="published" title="2018-02-11T19:20:00+08:00">
Published: 日 11 二月 2018
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By <a class="url fn" href="/author/andrew.html">andrew</a>
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<p>In <a href="/category/python.html">python</a>.</p>
</footer><!-- /.post-info --> <p>pandas有强大的excel数据处理和导入处理功能，本文简单介绍pandas在csv和excel等格式方面处理的应用及绘制图表等功能。</p>
<p>pandas处理excel依赖xlutils, OpenPyXL, XlsxWriter等库。</p>
<ul>
<li>
<p>python处理excel库的参考：https://github.com/china-testing/python-api-tesing</p>
</li>
<li>
<p>本文代码地址：https://github.com/china-testing/python-api-tesing/tree/master/pandas/excel_demo</p>
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<p><a href="https://china-testing.github.io/">本文最新版本地址</a></p>
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</ul>
<p>更多参考资料：</p>
<p>https://www.dataquest.io/blog/excel-and-pandas/</p>
<p><a href="https://realpython.com/blog/python/working-with-large-excel-files-in-pandas/">Using Pandas to Read Large Excel Files in Python</a> <a href="https://python.freelycode.com/contribution/detail/38">中文</a></p>
<p>Foundations for Analytics with Python From Non-Programmer to Hacker - 2016.pdf …</p>
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