Python_training-chinese.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Python 教程\n",
    "===\n",
    "陈同 (chentong_biology@163.com)\n",
    "=============\n",
    "\n",
    "\n",
    "欢迎来到Python的世界，本教程将带你遨游`Python`，领悟`Python`的魅力。本教程专注于帮助初学者，尤其是生物信息分析人员快速学会`Python`的常用功能和使用方式，因此只精选了部分`Python`的功能，请额外参考Python经典教程[A byte of python](http://www.byteofpython.info/)和它的[中文版](http://woodpecker.org.cn/abyteofpython_cn/chinese/index.html) 来更好的理解*Python*. 本文档的概念和文字描述参考了A byte of python(中文版)，特此感谢。\n",
    "\n",
    "This work is licensed under a <a rel=\"license\" href=\"http://creativecommons.org/licenses/by-nc-sa/2.0/\">Creative Commons Attribution-NonCommercial-ShareAlike 2.0 Generic License</a>.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 目录"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. [背景介绍](#背景介绍)\n",
    "    1. [编程开篇](#编程开篇)\n",
    "    1. [为什么学习Python](#为什么学习Python)\n",
    "    2. [如何安装Python](#如何安装Python)\n",
    "    3. [如何运行Python命令和脚本](#如何运行Python命令和脚本)\n",
    "    4. [使用什么编辑器写Python脚本](#使用什么编辑器写Python脚本)\n",
    "2. [Python程序事例](#Python程序事例)\n",
    "2. [Python基本语法](#Python语法)\n",
    "    1. [层级缩进](#层级缩进)\n",
    "    2. [变量、数据结构、流程控制](#变量、数据结构、流程控制)\n",
    "        1. [数值变量操作](#数值变量操作)\n",
    "        2. [字符串变量操作](#字符串变量操作)\n",
    "        3. [列表操作](#列表操作)\n",
    "        4. [元组操作](#元组操作)\n",
    "        4. [Range使用](#Range使用)\n",
    "        5. [字典操作](#字典操作)\n",
    "3. [输入输出](#输入输出)\n",
    "    1. [交互式输入输出](#交互式输入输出)\n",
    "    2. [文件读写](#文件读写)\n",
    "4. [实战练习（一）](#实战练习（一）)\n",
    "    1. [背景知识](#背景知识)\n",
    "    2. [作业(一)](#作业（一）)\n",
    "5. [函数操作](#函数操作)\n",
    "    1. [函数操作](#函数操作)\n",
    "    2. [作业（二）](#作业（二）)\n",
    "6. [模块](#模块)\n",
    "7. [命令行参数](#命令行参数)\n",
    "    1. [命令行参数](#命令行参数)\n",
    "    2. [作业（三）](#作业（三）)\n",
    "8. [更多Python内容](#更多Python内容)\n",
    "    1. [单语句块](#单语句块)\n",
    "    2. [列表综合，生成新列表的简化的for循环](#列表综合)\n",
    "    3. [lambda, map, filer, reduce (保留节目)](#lmfr)\n",
    "    4. [exec, eval (执行字符串python语句, 保留节目)](#ee)\n",
    "9. [Reference](#Reference)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 背景介绍"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 编程开篇"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A：最近在看什么书？\n",
    "\n",
    "B：编程。\n",
    "\n",
    "A：沈从文的那本啊。\n",
    "\n",
    "B：……\n",
    "\n",
    "<img src=\"D:/project/GitHub/PBR_training/Image/biancheng.jpg\"></img>\n",
    "\n",
    "---\n",
    "\n",
    "\n",
    "\n",
    "C：最近在学一门新语言，Python。\n",
    "\n",
    "D：那是哪个国家的语言？\n",
    "\n",
    "C：……"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 为什么学习Python"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* 语法简单 \n",
    "    \n",
    "    Python语言写作的程序就像自然语言构建的伪代码一样，“所见即所想”。读`Python`代码就像读最简单的英文短文一样，写`Python`代码比写英文文章都要简单，“所想即所写”。很多刚学习`Python`的朋友都觉得很不可思议，原来怎么想怎么写出来就对了。\n",
    "\n",
    "* 功能强大\n",
    "    \n",
    "    现在程序语言的发展已经很成熟，每一种程序语言都能实现其它程序语言的全部功能。因此就程序语言本身来讲，功能都相差不大。`Python`语言的功能强大在于其活跃的社区和强大的第三方模块支持，使其作为科学计算的能力越来越强。\n",
    "\n",
    "* 可扩展性好\n",
    "    \n",
    "    能与C完美的融合，加快运行速度。可用加速模块有`Cython`, `PyPy`, `Pyrex`, `Psyco`等."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 如何安装Python"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Python社区有很多功能很好的包，但逐个安装需要解决繁杂的依赖关系。通常我会推荐安装已经做好的集成包，一劳永逸的解决后续问题。这儿推荐的两个集成包有完全免费的[Anaconda](https://store.continuum.io/cshop/anaconda/)和对学术用户、教育用户免费的[Canopy](https://www.enthought.com/products/canopy/). 这两个分发包集成了常用的数值计算、图形处理、可视化等工具包如`IPython`, `matplotlib`, `numpy`, `scipy`, 而且设定了更简单的安装Python模块的方法，可以节省大量的安装时间。 "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 如何运行Python命令和脚本"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* 对于初学者，本手册推荐直接在`IPython Notebook`下学习`Python`命令和脚本。我们这套教程也是用`IPython Notebook`写作而成，里面的代码可以随时修改和运行，并能同时记录你的脚本和输出，符合现在流行的“可重复性计算”的概念。\n",
    "    * Linux/Unix用户直接在终端(Terminal)进入你的目标文件夹`cd /working_dir`[回车]，然后在终端输入`Ipython notebook`[回车]即可启动`Ipython notebook`。\n",
    "    * Windows用户可以新建一个`Ipython_notebook.bat`文件(新建一个txt文件，写入内容后修改后缀为`.bat`。若不能修改后缀，请Google搜索“Window是如何显示文件扩展名”)，并写入以下内容（注意把前两行的*盘符*和*路径*替换为你的工作目录），双击即可运行。\n",
    "```\n",
    "        D:\n",
    "        cd PBR_training\n",
    "        ipython notebook\n",
    "        pause\n",
    "```\n",
    "    * `Ipython notebook`启动后会打开默认的浏览器（需要在图形用户界面下工作），这时可以`新建`或`打开`相应路径下的ipynb文件。\n",
    "    \n",
    "* 对于LInux或Unix用户，直接在终端输入 `python` 然后回车即可打开交互式`python`解释器，如下图所示。在这个解释器了敲入任何合法的`python`语句即可执行。此外，所有的命令还可以存储到一个文件一起执行，如下图所示。我们有一个包含`python`程序的文件`test.py`，我们只要在终端输入`python test.py`并回车就可以运行这个文件。同时我们也可在终端通过输入`chmod 755 test.py`赋予程序`test.py`可执行权限，并在终端输入`./test.py`运行`Python`脚本。更多Linux下的高级使用和Linux命令使用请见教程Bash_training-chinese.ipynb。  <img src=\"Image/Python_start_run_linux.png\">\n",
    "\n",
    "* 对于Windows用户，可以通过“Windows键+R”调出“Run”窗口并输入“cmd”打开Windows命令解释器，输入`python`即可打开交互式`python`解释器。同时也可以双击安装后的软件的快捷方式打开图形界面的`Python`解释器，可以处理交互式命令和导入Python文件并执行。下图所示是Canopy的界面：<img src=\"Image/Python_start_run_winCanopy.png\">\n",
    "\n",
    "* 对于交互式`Python`解释器，在使用结束后，通过键盘组合键`Ctrl-d` (Linux/Unix)或`Ctrl-z` (Windows)关闭。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 使用什么编辑器写Python脚本"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "在你学成之后，可能主要操作都在服务器完成，而且日常工作一般会以脚本的形式解决。我个人推荐使用[Vim](http://www.vim.org/download.php)来写作Python脚本。\n",
    "\n",
    "Linux下`vim`的配置文件可从[我的 github](https://github.com/Tong-Chen/vim)下载，Windows版可从[我的百度云](http://pan.baidu.com/s/1kT5KIN1) 下载。 "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Python程序事例"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ">NM_001011874 gene=Xkr4 CDS=151-2091\n",
      "gcggcggcgggcgagcgggcgctggagtaggagctggggagcggcgcggccggggaaggaagccagggcg\n",
      "aggcgaggaggtggcgggaggaggagacagcagggacaggTGTCAGATAAAGGAGTGCTCTCCTCCGCTG\n",
      "CCGAGGCATCATGGCCGCTAAGTCAGACGGGAGGCTGAAGATGAAGAAGAGCAGCGACGTGGCGTTCACC\n",
      "CCGCTGCAGAACTCGGACAATTCGGGCTCTGTGCAAGGACTGGCTCCAGGCTTGCCGTCGGGGTCCGGAG\n",
      ">NM_001195662 gene=Rp1 CDS=55-909\n",
      "AAGCTCAGCCTTTGCTCAGATTCTCCTCTTGATGAAACAAAGGGATTTCTGCACATGCTTGAGAAATTGC\n",
      "AGGTCTCACCCAAAATGAGTGACACACCTTCTACTAGTTTCTCCATGATTCATCTGACTTCTGAAGGTCA\n",
      "AGTTCCTTCCCCTCGCCATTCAAATATCACTCATCCTGTAGTGGCTAAACGCATCAGTTTCTATAAGAGT\n",
      "GGAGACCCACAGTTTGGCGGCGTTCGGGTGGTGGTCAACCCTCGTTCCTTTAAGACTTTTGACGCTCTGC\n",
      "TGGACAGTTTATCCAGGAAGGTACCCCTGCCCTTTGGGGTAAGGAACATCAGCACGCCCCGTGGACGACA\n",
      "CAGCATCACCAGGCTGGAGGAGCTAGAGGACGGCAAGTCTTATGTGTGCTCCCACAATAAGAAGGTGCTG\n",
      ">NM_011283 gene=Rp1 CDS=128-6412\n",
      "AATAAATCCAAAGACATTTGTTTACGTGAAACAAGCAGGTTGCATATCCAGTGACGTTTATACAGACCAC\n",
      "ACAAACTATTTACTCTTTTCTTCGTAAGGAAAGGTTCAACTTCTGGTCTCACCCAAAATGAGTGACACAC\n",
      "CTTCTACTAGTTTCTCCATGATTCATCTGACTTCTGAAGGTCAAGTTCCTTCCCCTCGCCATTCAAATAT\n",
      "CACTCATCCTGTAGTGGCTAAACGCATCAGTTTCTATAAGAGTGGAGACCCACAGTTTGGCGGCGTTCGG\n",
      "GTGGTGGTCAACCCTCGTTCCTTTAAGACTTTTGACGCTCTGCTGGACAGTTTATCCAGGAAGGTACCCC\n",
      "TGCCCTTTGGGGTAAGGAACATCAGCACGCCCCGTGGACGACACAGCATCACCAGGCTGGAGGAGCTAGA\n",
      "GGACGGCAAGTCTTATGTGTGCTCCCACAATAAGAAGGTGCTGCCAGTTGACCTGGACAAGGCCCGCAGG\n",
      "CGCCCTCGGCCCTGGCTGAGTAGTCGCTCCATAAGCACGCATGTGCAGCTCTGTCCTGCAACTGCCAATA\n",
      "TGTCCACCATGGCACCTGGCATGCTCCGTGCCCCAAGGAGGCTCGTGGTCTTCCGGAATGGTGACCCGAA\n",
      ">NM_0112835 gene=Rp1 CDS=128-6412\n",
      "AATAAATCCAAAGACATTTGTTTACGTGAAACAAGCAGGTTGCATATCCAGTGACGTTTATACAGACCAC\n",
      "ACAAACTATTTACTCTTTTCTTCGTAAGGAAAGGTTCAACTTCTGGTCTCACCCAAAATGAGTGACACAC\n",
      "CTTCTACTAGTTTCTCCATGATTCATCTGACTTCTGAAGGTCAAGTTCCTTCCCCTCGCCATTCAAATAT\n",
      "CACTCATCCTGTAGTGGCTAAACGCATCAGTTTCTATAAGAGTGGAGACCCACAGTTTGGCGGCGTTCGG\n",
      "GTGGTGGTCAACCCTCGTTCCTTTAAGACTTTTGACGCTCTGCTGGACAGTTTATCCAGGAAGGTACCCC\n",
      "TGCCCTTTGGGGTAAGGAACATCAGCACGCCCCGTGGACGACACAGCATCACCAGGCTGGAGGAGCTAGA\n",
      "GGACGGCAAGTCTTATGTGTGCTCCCACAATAAGAAGGTGCTGCCAGTTGACCTGGACAAGGCCCGCAGG\n",
      "CGCCCTCGGCCCTGGCTGAGTAGTCGCTCCATAAGCACGCATGTGCAGCTCTGTCCTGCAACTGCCAATA\n",
      "TGTCCACCATGGCACCTGGCATGCTCCGTGCCCCAAGGAGGCTCGTGGTCTTCCGGAATGGTGACCCGAA\n"
     ]
    }
   ],
   "source": [
    "#假如我们有如下FASTA格式的文件，我们想把多行序列合并为一行，怎么做？\n",
    "for line in open(\"data/test2.fa\"):\n",
    "    print line,"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ">NM_011283 gene=Rp1 CDS=128-6412\n",
      "AATAAATCCAAAGACATTTGTTTACGTGAAACAAGCAGGTTGCATATCCAGTGACGTTTATACAGACCACACAAACTATTTACTCTTTTCTTCGTAAGGAAAGGTTCAACTTCTGGTCTCACCCAAAATGAGTGACACACCTTCTACTAGTTTCTCCATGATTCATCTGACTTCTGAAGGTCAAGTTCCTTCCCCTCGCCATTCAAATATCACTCATCCTGTAGTGGCTAAACGCATCAGTTTCTATAAGAGTGGAGACCCACAGTTTGGCGGCGTTCGGGTGGTGGTCAACCCTCGTTCCTTTAAGACTTTTGACGCTCTGCTGGACAGTTTATCCAGGAAGGTACCCCTGCCCTTTGGGGTAAGGAACATCAGCACGCCCCGTGGACGACACAGCATCACCAGGCTGGAGGAGCTAGAGGACGGCAAGTCTTATGTGTGCTCCCACAATAAGAAGGTGCTGCCAGTTGACCTGGACAAGGCCCGCAGGCGCCCTCGGCCCTGGCTGAGTAGTCGCTCCATAAGCACGCATGTGCAGCTCTGTCCTGCAACTGCCAATATGTCCACCATGGCACCTGGCATGCTCCGTGCCCCAAGGAGGCTCGTGGTCTTCCGGAATGGTGACCCGAA\n",
      ">NM_0112835 gene=Rp1 CDS=128-6412\n",
      "AATAAATCCAAAGACATTTGTTTACGTGAAACAAGCAGGTTGCATATCCAGTGACGTTTATACAGACCACACAAACTATTTACTCTTTTCTTCGTAAGGAAAGGTTCAACTTCTGGTCTCACCCAAAATGAGTGACACACCTTCTACTAGTTTCTCCATGATTCATCTGACTTCTGAAGGTCAAGTTCCTTCCCCTCGCCATTCAAATATCACTCATCCTGTAGTGGCTAAACGCATCAGTTTCTATAAGAGTGGAGACCCACAGTTTGGCGGCGTTCGGGTGGTGGTCAACCCTCGTTCCTTTAAGACTTTTGACGCTCTGCTGGACAGTTTATCCAGGAAGGTACCCCTGCCCTTTGGGGTAAGGAACATCAGCACGCCCCGTGGACGACACAGCATCACCAGGCTGGAGGAGCTAGAGGACGGCAAGTCTTATGTGTGCTCCCACAATAAGAAGGTGCTGCCAGTTGACCTGGACAAGGCCCGCAGGCGCCCTCGGCCCTGGCTGAGTAGTCGCTCCATAAGCACGCATGTGCAGCTCTGTCCTGCAACTGCCAATATGTCCACCATGGCACCTGGCATGCTCCGTGCCCCAAGGAGGCTCGTGGTCTTCCGGAATGGTGACCCGAA\n",
      ">NM_001011874 gene=Xkr4 CDS=151-2091\n",
      "gcggcggcgggcgagcgggcgctggagtaggagctggggagcggcgcggccggggaaggaagccagggcgaggcgaggaggtggcgggaggaggagacagcagggacaggTGTCAGATAAAGGAGTGCTCTCCTCCGCTGCCGAGGCATCATGGCCGCTAAGTCAGACGGGAGGCTGAAGATGAAGAAGAGCAGCGACGTGGCGTTCACCCCGCTGCAGAACTCGGACAATTCGGGCTCTGTGCAAGGACTGGCTCCAGGCTTGCCGTCGGGGTCCGGAG\n",
      ">NM_001195662 gene=Rp1 CDS=55-909\n",
      "AAGCTCAGCCTTTGCTCAGATTCTCCTCTTGATGAAACAAAGGGATTTCTGCACATGCTTGAGAAATTGCAGGTCTCACCCAAAATGAGTGACACACCTTCTACTAGTTTCTCCATGATTCATCTGACTTCTGAAGGTCAAGTTCCTTCCCCTCGCCATTCAAATATCACTCATCCTGTAGTGGCTAAACGCATCAGTTTCTATAAGAGTGGAGACCCACAGTTTGGCGGCGTTCGGGTGGTGGTCAACCCTCGTTCCTTTAAGACTTTTGACGCTCTGCTGGACAGTTTATCCAGGAAGGTACCCCTGCCCTTTGGGGTAAGGAACATCAGCACGCCCCGTGGACGACACAGCATCACCAGGCTGGAGGAGCTAGAGGACGGCAAGTCTTATGTGTGCTCCCACAATAAGAAGGTGCTG\n"
     ]
    }
   ],
   "source": [
    "aDict = {}\n",
    "for line in open('data/test2.fa'):\n",
    "    if line[0] == '>':\n",
    "        key = line.strip()\n",
    "        aDict[key] = []\n",
    "    else:\n",
    "        aDict[key].append(line.strip())\n",
    "#------------------------------------------\n",
    "for key, valueL in aDict.items():\n",
    "    print key\n",
    "    print ''.join(valueL)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Python语法"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 层级缩进"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* 合适的缩进。空白在Python中是很重要的，它称为缩进。在逻辑行首的空白（空格和制表符）用来决定逻辑行的缩进层次，从而用来决定语句的分组。这意味着同一层次的语句必须有相同的缩进。每一组这样的语句称为一个块。通常的缩进为4个空格, 在`Ipython Notebook`中为一个`Tab`键。\n",
    "  \n",
    " 从下面这两个例子可以看出错误的缩进类型和对应的提示。\n",
    " * \"unexpected indent\" 表示在不该出现空白的地方多了空白，并且指出问题出在第三行(line 3)。\n",
    " * \"expected an indented block\" 表示应该有缩进的地方未缩进，也指出了问题所在行。\n",
    " * \"unindent does not match any outer indentation level\" 表示缩进出现了不一致，问题通常会在指定行**及其前面的行**。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "IndentationError",
     "evalue": "unexpected indent (<ipython-input-123-085115ffae95>, line 3)",
     "output_type": "error",
     "traceback": [
      "\u001b[1;36m  File \u001b[1;32m\"<ipython-input-123-085115ffae95>\"\u001b[1;36m, line \u001b[1;32m3\u001b[0m\n\u001b[1;33m    b = '我前面有个空格……'\u001b[0m\n\u001b[1;37m    ^\u001b[0m\n\u001b[1;31mIndentationError\u001b[0m\u001b[1;31m:\u001b[0m unexpected indent\n"
     ]
    }
   ],
   "source": [
    "print \"不合适的缩进会引发错误，不该有的缩进\"\n",
    "a = 'No indent'\n",
    " b = '我前面有个空格……'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "IndentationError",
     "evalue": "expected an indented block (<ipython-input-2-1b9e89963ac3>, line 5)",
     "output_type": "error",
     "traceback": [
      "\u001b[1;36m  File \u001b[1;32m\"<ipython-input-2-1b9e89963ac3>\"\u001b[1;36m, line \u001b[1;32m5\u001b[0m\n\u001b[1;33m    print \"我应该被缩进，我从属于for循环!!!\\n\"\u001b[0m\n\u001b[1;37m        ^\u001b[0m\n\u001b[1;31mIndentationError\u001b[0m\u001b[1;31m:\u001b[0m expected an indented block\n"
     ]
    }
   ],
   "source": [
    "print \"不合适的缩进，应该有，却漏掉的缩进\"\n",
    "a = [1,2,3]\n",
    "\n",
    "for i in a:\n",
    "print \"我应该被缩进，我从属于for循环!!!\\n\"\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 203,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "IndentationError",
     "evalue": "unindent does not match any outer indentation level (<ipython-input-203-1af46ff5a29f>, line 5)",
     "output_type": "error",
     "traceback": [
      "\u001b[1;36m  File \u001b[1;32m\"<ipython-input-203-1af46ff5a29f>\"\u001b[1;36m, line \u001b[1;32m5\u001b[0m\n\u001b[1;33m    print i + 1, \"为什么我的缩进跟其它行不一样呢，我的空格被谁吃了？\"\u001b[0m\n\u001b[1;37m                                            \n^\u001b[0m\n\u001b[1;31mIndentationError\u001b[0m\u001b[1;31m:\u001b[0m unindent does not match any outer indentation level\n"
     ]
    }
   ],
   "source": [
    "a = [1,2, 3]\n",
    "if a:\n",
    "    for i in a:\n",
    "        print i\n",
    "       print i + 1, \"为什么我的缩进跟其它行不一样呢，我的空格被谁吃了？\"\n",
    "         print i + 1, \"为什么我的缩进跟其它行不一样呢，谁给了我个空格？\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 变量、数据结构、流程控制"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* 常量，指固定的数字或字符串，如`2`, `2.9`, `Hello world`等。 \n",
    "\n",
    "* 变量，存储了数字或字符串的事物称为变量，它可以被赋值或被修改。简单的可以理解为变量是一个盒子，你可以把任何东西放在里面，通过盒子的名字来取出盒子内的东西。\n",
    "    * 数值变量：存储了数的变量。     \n",
    "    * 字符串变量：存储了字符串的变量。字符串变量的名字最好不为`str`，可以使用`aStr`。\n",
    "    * 列表 (list): list是处理一组*有序*项目的数据结构，即你可以在一个列表中存储一个 *序列* 的项目。假想你有一个购物列表，上面记载着你要买的东西，你就容易理解列表了。只不过在你的购物表上，可能每样东西都独自占有一行，而在Python中，你在每个项目之间用逗号分割。列表中的项目应该包括在**方括号**中，这样Python就知道你是在指明一个列表。一旦你创建了一个列表，你可以添加、删除或是搜索列表中的项目。由于你可以增加或删除项目，我们说列表是 *可变的* 数据类型，即这种类型是可以被改变的。列表变量的名字最好不为`list`，可以使用`aList`。\n",
    "    * 元组 (set，集合)：元组和列表十分类似，但元组中不允许重复值出现。元组通过**圆括号中用逗号分割的项目**定义。元组通常用在使语句或用户定义的函数能够安全地采用一组值的时候，即被使用的元组的值不会改变。元组变量的名字最好不为`set`，可以使用`aSet`。\n",
    "    * 字典 (dict): 字典类似于你通过联系人名字查找地址和联系人详细情况的地址簿，即，我们把键（名字）和值（详细情况）联系在一起。注意，键必须是唯一的，就像如果有两个人恰巧同名的话，你无法找到正确的信息。多个键可以指向同一个值。当一个键需要指向多个值时，这些值需要放在列表、元组或字典里面。注意，你只能使用不可变的对象（字符串，数字，元组）来作为字典的键，但是可以用不可变或可变的对象作为字典的值。键值对在字典中以这样的方式标记：d = {key1 : value1, key2 : value2 }。注意它们的键/值对用冒号分割，而各个对用逗号分割，所有这些都包括在**花括号**中。记住字典中的键/值对是没有顺序的。如果你想要一个特定的顺序，那么你应该在使用前自己对它们排序。列表变量的名字最好不为`dict`，可以使用`aDict`。\n",
    "    * 序列：列表、元组、字符串都是一种序列格式。同时还可以使用range来产生序列。序列的两个主要操作时*索引操作*和*切片操作*。\n",
    "\n",
    "* 标示符  \n",
    "      * 变量的名字被称为标示符。标识符对大小写敏感，第一个字符必须是字母表中的字母（大写或小写）或者一个下划线（\\_），其它部分额外包含数字。有效的标示符有: `abc`, `_abc`, `a_b_2`, `__23`等。无效的标示符有: `2a`, `3b`。\n",
    "      * 标示符最好不使用Python内置的关键字，如`str`, `list`, `int`, `def`, `split`, `dict`等。\n",
    "      * 标示符最好能言词达意，即展示变量的类型，又带有变量的实际含义。如`line`表示文件的一行，`lineL`表示存有从文件读入的每一行的列表。\n",
    "\n",
    "* 控制流\n",
    "    * `if`语句\n",
    "      \n",
    "      `if`语句用来检验一个条件，如果条件为真，我们运行一块语句（称为 `if-块`），否则我们处理另外一块语句（称为 `else-块`）。`else` 从句是可选的。如果有多个条件，中间使用`elif`。\n",
    "      \n",
    "      举个例子：“买五个包子，如果看到卖西瓜的，买一个”——最后程序猿买了一个包子”\n",
    "      ```\n",
    "      买包子 = 5\n",
    "      if 看到卖西瓜的:\n",
    "          买包子 = 1\n",
    "      ```\n",
    "      \n",
    "    * `For`语句\n",
    "      \n",
    "      for..in是一个循环语句，它在一序列的对象上递归,即逐一使用队列中的每个项目。\n",
    "      \n",
    "    * `While`语句\n",
    "      \n",
    "      只要在一个条件为真的情况下，`while`语句允许你重复执行一块语句。`while`语句是所谓 *循环* 语句的一个例子。`while`语句有一个可选的`else`从句。\n",
    "       \n",
    "    * `break`语句是用来 *终止* 循环语句的，即哪怕循环条件没有成为False或序列还没有被完全递归，也停止执行循环语句。\n",
    "    \n",
    "      一个重要的注释是，如果你从`for`或`while`循环中 *终止* ，任何对应的循环else块将不执行。\n",
    "      \n",
    "    * `continue`语句被用来告诉`Python`跳过当前循环块中的剩余语句，然后 *继续* 进行下一轮循环。\n",
    "    \n",
    "    * 逻辑运算符 `and`, `or`, `not`。 \n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 数值变量操作"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "数值变量\n",
      "5\n",
      "\n",
      "The type of a is <type 'int'>\n"
     ]
    }
   ],
   "source": [
    "print \"数值变量\"\n",
    "a = 5   #注意等号两边的空格，为了易于辨识，操作符两侧最后有空格，数量不限\n",
    "print a\n",
    "\n",
    "print\n",
    "print \"The type of a is\", type(a)\n",
    "\n",
    "#print \"这是保留节目，通常判断变量的类型使用的不是type是isinstance.\"\n",
    "#print \"a is an int, \", isinstance(a,int)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 205,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "比较数值的大小\n",
      "a is larger than 4.\n"
     ]
    }
   ],
   "source": [
    "#判断\n",
    "print \"比较数值的大小\"\n",
    "a = 5 \n",
    "\n",
    "#注意大于号两边的空格，为了易于辨识，操作符两侧最后有空格，数量不限\n",
    "if a > 4: \n",
    "    print \"a is larger than 4.\"\n",
    "elif a == 4:\n",
    "    print \"a is equal to 4.\"\n",
    "else:\n",
    "    print \"a is less than 4\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 206,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "给定数值变量a和b的值，通过判断和重新赋值使得a的值小，b的值大\n",
      "3\n",
      "5\n"
     ]
    }
   ],
   "source": [
    "print \"给定数值变量a和b的值，通过判断和重新赋值使得a的值小，b的值大\"\n",
    "a = 5\n",
    "b = 3\n",
    "\n",
    "if a > b:\n",
    "    c = a\n",
    "    a = b\n",
    "    b = c\n",
    "    #print \"保留节目，Python特有，不通过中间变量直接做交换，神奇吧！！\"\n",
    "    #a,b = b,a\n",
    "#-------------------\n",
    "print a \n",
    "print b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "#数值运算, 符合传统的优先级，需要使用括号来改变优先级，\n",
      "和小学学的数学一模一样！！\n",
      "a + b = 8\n",
      "a * b = 15\n",
      "a / b = 1\n",
      "2 * (a+b) = 16\n",
      "取余数： a % b = 2\n",
      "取余数是很好的判断循环的地方，因为每个固定的周期余数就会循环一次\n"
     ]
    }
   ],
   "source": [
    "print '''#数值运算, 符合传统的优先级，需要使用括号来改变优先级，\n",
    "和小学学的数学一模一样！！'''\n",
    "a = 5\n",
    "b = 3\n",
    "\n",
    "print \"a + b =\", a + b\n",
    "print \"a * b =\", a * b\n",
    "print \"a / b =\", a/b  # 1\n",
    "print \"2 * (a+b) =\", 2 * (a+b)\n",
    "print \"取余数： a % b =\", a % b\n",
    "print \"取余数是很好的判断循环的地方，因为每个固定的周期余数就会循环一次\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 字符串变量操作"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 208,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "字符串变量\n",
      "The string a is: Hello, welcome to Python\n",
      "\n",
      "The length of this string <Hello, welcome to Python> is 24\n",
      "\n",
      "The type of a is <type 'str'>\n"
     ]
    }
   ],
   "source": [
    "print \"字符串变量\"\n",
    "\n",
    "a = \"Hello, welcome to Python\"\n",
    "\n",
    "#a = 123\n",
    "#a = str(a)\n",
    "\n",
    "print \"The string a is:\", a\n",
    "print\n",
    "\n",
    "#占位符\n",
    "print \"The length of this string <%s> is %d\" % (a, len(a))\n",
    "print\n",
    "\n",
    "print \"The type of a is\", type(a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The string a is: 大事赖独断而不赖众谋\n",
      "\n",
      "The length of this string <大事赖独断而不赖众谋> is 30\n",
      "\n"
     ]
    }
   ],
   "source": [
    "a = \"大事赖独断而不赖众谋\"\n",
    "print \"The string a is:\", a\n",
    "print\n",
    "\n",
    "print \"The length of this string <%s> is %d\" % (a, len(a))\n",
    "print"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 209,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "取出字符串的第一个字符、最后一个字符、中间部分字符\n",
      "The first character of a is H\n",
      "\n",
      "The first five characters of a are Hello\n",
      "\n",
      "The last character of a is n\n",
      "\n",
      "The last character of a is n\n",
      "\n",
      "\n",
      "这部分很重要啊，字符的索引和切片操作是及其常用的。\n"
     ]
    }
   ],
   "source": [
    "a = \"Hello, welcome to Python\"\n",
    "\n",
    "print \"取出字符串的第一个字符、最后一个字符、中间部分字符\"\n",
    "print \"The first character of a is %s\\n\" % a[0]\n",
    "\n",
    "print \"The first five characters of a are %s\\n\" % a[0:5]\n",
    "\n",
    "print \"The last character of a is %s\\n\" % a[-1]\n",
    "print \"The last character of a is %s\\n\" % a[len(a)-1]\n",
    "print \"\\n这部分很重要啊，字符串的索引和切片操作是及其常用的。\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "遍历字符串\n",
      "o\n",
      "a\n",
      "o\n",
      "a\n",
      "o\n",
      "a\n",
      "o\n",
      "a\n",
      "输出符合特定要求的字符的位置\n",
      "\n",
      "1\n",
      "3\n",
      "5\n",
      "7\n",
      "\n",
      "知道吗？不经意间我们写出了Python的\n",
      "一个内置的标准函数find或者index,而且功能还更强大\n"
     ]
    }
   ],
   "source": [
    "a = \"oaoaoaoa\"\n",
    "\n",
    "print \"遍历字符串\"\n",
    "for i in a:\n",
    "    print i\n",
    "\n",
    "print \"输出符合特定要求的字符的位置\"\n",
    "print\n",
    "pos = 0\n",
    "for i in a:\n",
    "    pos += 1\n",
    "    if i == 'o':\n",
    "        print pos\n",
    "    #-------------------\n",
    "#-----------------------\n",
    "print '''\\n知道吗？不经意间我们写出了Python的\n",
    "一个内置的标准函数find或者index,而且功能还更强大'''\n",
    "\n",
    "print '''\\n自己尝试实现程序语言内建的函数是学习程序语言\n",
    "的很好方法。'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 211,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "我们看看用内置函数如何找到所有 o 的位置\n",
      "\n",
      "内置函数find只能确定最先出现的 o 的位置\n",
      "因此，我们要在发现 o 之后，截取其后的字符串，再执行find操作\n",
      "1\n",
      "3\n",
      "5\n",
      "7\n"
     ]
    }
   ],
   "source": [
    "print \"我们看看用内置函数如何找到所有 o 的位置\\n\"\n",
    "a = \"oaoaoaoa\"\n",
    "\n",
    "print \"内置函数find只能确定最先出现的 o 的位置\"\n",
    "pos = a.find('o')\n",
    "\n",
    "print \"因此，我们要在发现 o 之后，截取其后的字符串，再执行find操作\"\n",
    "while 1:\n",
    "    print pos + 1\n",
    "    new = a[pos+1:].find('o')\n",
    "    if new == -1:\n",
    "        break\n",
    "    pos = new + pos + 1\n",
    "#help(str)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 212,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "利用split分割字符串\n",
      "\n",
      "['a', 'b', 'c', 'd', 'e', 'f', 'g']\n",
      "\n",
      "使用split命令就可以把字符串分成列表了，想取用哪一列都随便我了。\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print\n",
    "print \"利用split分割字符串\\n\"\n",
    "str1 = \"a b c d e f g\"\n",
    "strL = str1.split(' ')\n",
    "print strL\n",
    "print \"\\n使用split命令就可以把字符串分成列表了，想取用哪一列都随便你了。\"\n",
    "#使用下面的命令查看可以对字符串进行的操作\n",
    "#help(str)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "字符串的连接\n",
      "\n",
      "Hello, Python\n",
      "\n",
      "原来字符串相加就可以连起来啊！\n",
      "\n",
      "注意，这不是连接字符串最好的方式。\n",
      "考虑到字符串是不可修改的，每次连接操作都是新开辟一个内存空间，\n",
      "把字符串存到里面，这样的连接操作执行几十万次会很影响运行速度。\n"
     ]
    }
   ],
   "source": [
    "print \"字符串的连接\\n\"\n",
    "\n",
    "a = \"Hello\"\n",
    "b = \"Python\"\n",
    "c = a + ', ' + b\n",
    "print c\n",
    "print \"\\n原来字符串相加就可以连起来啊！\\n\"\n",
    "print '''注意，这不是连接字符串最好的方式。\n",
    "考虑到字符串是不可修改的，每次连接操作都是新开辟一个内存空间，\n",
    "把字符串存到里面，这样的连接操作执行几十万次会很影响运行速度。'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "去除字符串中特定的字符。通常我们在文件中读取的一行都包含换行符，\n",
      "linux下为\\n \n",
      "\n",
      "Currently, the string <a> is ** oneline\n",
      "**. \n",
      " The length of string <a> is ** 8 **. \n",
      " 我为什么换到下一行了？\n",
      "\n",
      "Currently, the string <a> is ** oneline **. \n",
      " The length of string <a> is ** 7 **. \n",
      " 删掉了换行符后，少了个字符，而且没换行！\n",
      "\n",
      "Currently, the string <a> is ** neline **. \n",
      " The length of string <a> is ** 6 **. 又少了个字符！！\n",
      "\n",
      "Currently, the string <a> is ** li **. \n",
      " The length of string <a> is ** 2 **. 又少字符了！！\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print '''去除字符串中特定的字符。通常我们在文件中读取的一行都包含换行符，\n",
    "linux下为\\\\n \\n''' #\\\\转义字符\n",
    "\n",
    "a = \"oneline\\n\"\n",
    "print \"Currently, the string <a> is **\", a, \"**. \\\n",
    "\\n The length of string <a> is **\", len(a),\"**. \\\n",
    "\\n 我为什么换到下一行了？\\n\"\n",
    "\n",
    "a = a.strip()\n",
    "print \"Currently, the string <a> is **\", a, \"**. \\\n",
    "\\n The length of string <a> is **\", len(a),\"**. \\\n",
    "\\n 删掉了换行符后，少了个字符，而且没换行！\\n\"\n",
    "\n",
    "a = a.strip('o')\n",
    "print \"Currently, the string <a> is **\", a, \"**. \\\n",
    "\\n The length of string <a> is **\", len(a), \"**. \\\n",
    "又少了个字符！！\\n\"\n",
    "\n",
    "a = a.strip('one')\n",
    "print \"Currently, the string <a> is **\", a, \"**. \\\n",
    "\\n The length of string <a> is **\", len(a), \"**. \\\n",
    "又少字符了！！\\n\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "字符串的替换\n",
      "\n",
      "原始字符串是: Hello, Python\n",
      "\n",
      "替换后的字符串是: Welcome, Python\n",
      "\n",
      "HellO, PythOn\n",
      "所有的o都被替换了！\n",
      "\n",
      "如果我只第一个o怎么办呢？\n",
      "\n",
      "HellO, Python\n"
     ]
    }
   ],
   "source": [
    "print \"字符串的替换\\n\"\n",
    "\n",
    "a = \"Hello, Python\"\n",
    "b = a.replace(\"Hello\",\"Welcome\")\n",
    "print \"原始字符串是:\", a\n",
    "print\n",
    "print \"替换后的字符串是:\", b\n",
    "print\n",
    "\n",
    "c = a.replace(\"o\",\"O\")\n",
    "print c\n",
    "print \"所有的o都被替换了！\\n\"\n",
    "\n",
    "print \"如果我只替换第一个o怎么办呢？\\n\"\n",
    "c = a.replace(\"o\",\"O\",1)\n",
    "print c"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "忙处事为，常向闲中先检点，过举自稀\n",
      "动时念想，预从静里密操持，非心自息。\n"
     ]
    }
   ],
   "source": [
    "a = \"忙处事为，常向闲中先检点，过举自稀；动时念想，预从静里密操持，非心自息。\"\n",
    "print a.replace('；','\\n')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "print \"字符串帮助\"\n",
    "help(str)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "大小写判断和转换\n",
      "All elements in <Sdsdsd> is lowercase: False\n",
      "Transfer all elments in <Sdsdsd> to lowerse <sdsdsd>\n",
      "Transfer all elments in <Sdsdsd> to upperse <SDSDSD>\n"
     ]
    }
   ],
   "source": [
    "print \"大小写判断和转换\"\n",
    "a = 'Sdsdsd'\n",
    "print \"All elements in <%s> is lowercase: %s\" % (a, a.islower())\n",
    "print \"Transfer all elments in <%s> to lowerse <%s>\" % (a, a.lower())\n",
    "print \"Transfer all elments in <%s> to upperse <%s>\" % (a, a.upper())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 218,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "这个是个保留节目，有兴趣的看，无兴趣的跳过不影响学习\n",
      "字符串是不可修改的，同一个变量名字赋不同的只实际是产生了多个不同的变量。不同的变量名字赋同样的值，用于比较时相等，但引用不同的区域\n",
      "The memory index of b is 37117024\n",
      "The memory index of b is 56557120\n",
      "The memory index of b is 57454016\n",
      "The memory index of b is 57334640\n",
      "The memory index of b is 57334640\n",
      "The memory index of b is 57350952\n",
      "The memory index of b is 57201408\n",
      "The memory index of b is 57142240\n"
     ]
    }
   ],
   "source": [
    "print \"这个是个保留节目，有兴趣的看，无兴趣的跳过不影响学习\"\n",
    "print '''字符串是不可修改的，\n",
    "同一个变量名字赋不同的只实际是产生了多个不同的变量。\n",
    "不同的变量名字赋同样的值，用于比较时相等，但引用不同的区域'''\n",
    "\n",
    "\n",
    "b = \"123456\"\n",
    "#print b\n",
    "print \"The memory index of b is\", id(b)\n",
    "for i in range(1,15,2):\n",
    "    b = b + '123456'\n",
    "    #print b\n",
    "    print \"The memory index of b is\", id(b)\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 219,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "字符串转数组\n",
      "['a', 'r', 'r', 'a', 'y']\n",
      "yarra\n"
     ]
    }
   ],
   "source": [
    "print \"字符串转数组\"\n",
    "str1 = \"array\"\n",
    "print list(str1)\n",
    "a = list(str1)\n",
    "a.reverse()\n",
    "print ''.join(a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "数字字符串转数值\n",
      "1231 124\n",
      "124.5\n",
      "从文件或命令行参数中取出的数字都是字符串形式出现，\n",
      "做四则运算时要先用int 或 float转换。\n"
     ]
    }
   ],
   "source": [
    "print \"数字字符串转数值\"\n",
    "a = '123'\n",
    "print a+'1', int(a)+1\n",
    "a = '123.5'\n",
    "#print a + 1\n",
    "print float(a)+1\n",
    "print '''从文件或命令行参数中取出的数字都是字符串形式出现，\n",
    "做四则运算时要先用int 或 float转换。'''"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 列表操作"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 220,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "#构建一个数组\n",
      "[1, 2, 3, 4, 5]\n",
      "\n",
      "The first element is 1.\n",
      "\n",
      "The last element is 5.\n",
      "\n",
      "The first two elements are [1, 2]\n",
      "\n",
      "数组索引和切片操作与字符串是一样一样的，而且都很重要。\n"
     ]
    }
   ],
   "source": [
    "print \"#构建一个数组\"\n",
    "aList = [1,2,3,4,5]\n",
    "print aList\n",
    "print\n",
    "print \"The first element is %d.\" % aList[0]\n",
    "print\n",
    "print \"The last element is %d.\" % aList[-1]\n",
    "print\n",
    "print \"The first two elements are\", aList[:2]\n",
    "print \"\\n数组索引和切片操作与字符串是一样一样的，而且都很重要。\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 221,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "#向数组中增加元素\n",
      "[6]\n",
      "\n",
      "#向数组中增加一个数组\n",
      "\n",
      "[6, 'a', 'b', 'c']\n"
     ]
    }
   ],
   "source": [
    "aList = []\n",
    "print \"#向数组中增加元素\"\n",
    "aList.append(6)\n",
    "print aList\n",
    "\n",
    "print \"\\n#向数组中增加一个数组\"\n",
    "print \n",
    "bList = ['a','b','c']\n",
    "aList.extend(bList)\n",
    "print aList"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "在数组中删除元素\n",
      "\n",
      "[1, 2, 4, 3, 5]\n",
      "\n",
      "[1, 2, 4, 5]\n",
      "\n",
      "pop和remove是不一样的，remove是移除等于给定值的元素，\n",
      "pop是移除给定位置的元素\n",
      "\n"
     ]
    }
   ],
   "source": [
    "aList = [1,2,3,4,3,5]\n",
    "print \"在数组中删除元素\"\n",
    "aList.remove(3) #只删除第一个匹配的 3 \n",
    "print\n",
    "print aList\n",
    "\n",
    "aList.pop(3) #移除元素的下标为3的字符\n",
    "print\n",
    "print aList\n",
    "print '''\\npop和remove是不一样的，remove是移除等于给定值的元素，\n",
    "pop是移除给定位置的元素\\n'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 223,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "#遍历数组的每个元素\n",
      "\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "#输出数组中大于3的元素\n",
      "\n",
      "4\n",
      "5\n"
     ]
    }
   ],
   "source": [
    "aList = [1,2,3,4,5]\n",
    "\n",
    "print \"#遍历数组的每个元素\"\n",
    "print\n",
    "for ele in aList:\n",
    "    print ele\n",
    "\n",
    "print \"#输出数组中大于3的元素\"\n",
    "print \n",
    "\n",
    "for ele in aList:\n",
    "    if ele > 3:\n",
    "        print ele"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 224,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "#输出数组中大于3,且小于10的元素\n",
      "\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n"
     ]
    }
   ],
   "source": [
    "aList = [i for i in range(30)]\n",
    "print \"#输出数组中大于3,且小于10的元素\"\n",
    "print \n",
    "\n",
    "for ele in aList:\n",
    "    if ele > 3 and ele < 10: #逻辑与，当两个条件都符合时才输出\n",
    "        print ele"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "#输出数组中大于25,或小于5的元素\n",
      "\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "26\n",
      "27\n",
      "28\n",
      "29\n"
     ]
    }
   ],
   "source": [
    "aList = [i for i in range(30)]\n",
    "print \"#输出数组中大于25,或小于5的元素\"\n",
    "print \n",
    "\n",
    "for ele in aList:\n",
    "    if ele > 25 or ele < 5: #逻辑或，当两个条件满足一个时就输出\n",
    "        print ele"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "#输出数组中大于3,且小于10的元素\n",
      "\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n"
     ]
    }
   ],
   "source": [
    "aList = [i for i in range(30)]\n",
    "print \"#输出数组中大于3,且小于10的元素\"\n",
    "print \n",
    "\n",
    "for ele in aList:\n",
    "    #逻辑非，当不符合给定条件时才输出。\n",
    "    #对于这个例子就是ele不大于3时才输出，相当于 if ele <= 3:\n",
    "    if not ele > 3: \n",
    "        print ele"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "连接数组的每个元素（每个元素必须为字符串）\n",
      "[1, 2, 3, 4, 5]\n",
      "['1', '2', '3', '4', '5']\n",
      "1\t2\t3\t4\t5\n",
      "1:2:3:4:5\n",
      "12345\n",
      "\n",
      "先把字符串存到列表，再使用join连接，\n",
      "是最合适的连接大量字符串的方式\n"
     ]
    }
   ],
   "source": [
    "print \"连接数组的每个元素（每个元素必须为字符串）\"\n",
    "aList = [1,2,3,4,5] \n",
    "#print '\\t'.join(aList) #wrong\n",
    "\n",
    "print aList\n",
    "aList = [str(i) for i in aList]\n",
    "print aList\n",
    "print '\\t'.join(aList) \n",
    "\n",
    "print ':'.join(aList)\n",
    "print ''.join(aList)\n",
    "\n",
    "print '''\\n先把字符串存到列表，再使用join连接，\n",
    "是最合适的连接大量字符串的方式'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 228,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "数组反序\n",
      "[5, 4, 3, 2, 1]\n",
      "数组元素排序\n",
      "[1, 2, 3, 4, 5]\n"
     ]
    }
   ],
   "source": [
    "aList = [1,2,3,4,5]\n",
    "\n",
    "print \"数组反序\"\n",
    "aList.reverse()\n",
    "print aList\n",
    "\n",
    "print \"数组元素排序\"\n",
    "aList.sort()\n",
    "print aList\n",
    "\n",
    "#print \"lambda排序，保留节目\"\n",
    "#aList.sort(key=lambda x: x*(-1))\n",
    "#print aList"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 元组操作"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 229,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "构建一个元组\n",
      "set([1, 2, 3])\n",
      "增加一个元素\n",
      "set([1, 2, 3, 4])\n",
      "set([1, 2, 3, 4])\n"
     ]
    }
   ],
   "source": [
    "print \"构建一个元组\"\n",
    "aSet = set([1,2,3])\n",
    "print aSet\n",
    "\n",
    "print \"增加一个元素\"\n",
    "aSet.add(4)\n",
    "print aSet\n",
    "aSet.add(3)\n",
    "print aSet"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 230,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "采用转换为元组去除列表中的重复元素\n",
      "[1, 2, 1, 3, 1, 5, 2, 4, 3, 3, 6]\n",
      "set([1, 2, 3, 4, 5, 6])\n",
      "[1, 2, 3, 4, 5, 6]\n"
     ]
    }
   ],
   "source": [
    "print \"采用转换为元组去除列表中的重复元素\"\n",
    "aList = [1,2,1,3,1,5,2,4,3,3,6]\n",
    "print aList\n",
    "print set(aList)\n",
    "print list(set(aList))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Range使用"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 231,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "使用range，产生一系列的字符串\n",
      "0\n",
      "4\n",
      "8\n",
      "12\n",
      "通过指定步长产生4的倍数的数\n",
      "0\n",
      "4\n",
      "8\n",
      "12\n"
     ]
    }
   ],
   "source": [
    "print \"使用range，产生一系列的字符串\"\n",
    "for i in range(16):\n",
    "    if i % 4 == 0:\n",
    "        print i\n",
    "print \"通过指定步长产生4的倍数的数\"\n",
    "for i in range(0,16,4):\n",
    "    print i"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 字典操作"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 232,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "#构建一个字典\n",
      "打印字典\n",
      "{'a': 'b', 1: 2, 3: 4, 'd': 'c'}\n",
      "向字典中添加键值对\n",
      "{'a': 'b', 1: 2, 3: 4, 'e': 'f', 'd': 'c', 5: 6}\n"
     ]
    }
   ],
   "source": [
    "print \"#构建一个字典\"\n",
    "aDict = {1:2,3:4,'a':'b','d':'c'}\n",
    "\n",
    "print \"打印字典\"\n",
    "print aDict\n",
    "\n",
    "print \"向字典中添加键值对\"\n",
    "aDict[5] = 6\n",
    "aDict['e'] = 'f'\n",
    "print aDict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 233,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "输出字典的键值对(key-value)\n",
      "a b\n",
      "1 2\n",
      "3 4\n",
      "e f\n",
      "d c\n",
      "5 6\n"
     ]
    }
   ],
   "source": [
    "print\n",
    "aDict = {1:2,3:4,'a':'b','d':'c'}\n",
    "print \"输出字典的键值对(key-value)\"\n",
    "for key, value in aDict.items():\n",
    "    print key,value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 234,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "有序输出字典的键值对(key-value)\n",
      "['a', 1, 3, 'e', 'd', 5]\n",
      "[1, 3, 5, 'a', 'd', 'e']\n",
      "1 2\n",
      "3 4\n",
      "5 6\n",
      "a b\n",
      "d c\n",
      "e f\n"
     ]
    }
   ],
   "source": [
    "print \"有序输出字典的键值对(key-value)\"\n",
    "aDict = {1:2,3:4,'a':'b','d':'c'}\n",
    "keyL = aDict.keys()\n",
    "print keyL\n",
    "keyL.sort()\n",
    "print keyL\n",
    "for key in keyL:\n",
    "    print key, aDict[key]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 235,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "字典的value可以是一个列表\n",
      "{}\n",
      "{'key': []}\n",
      "{'key': [1, 2]}\n",
      "\n",
      "key2\n",
      "\t3\n",
      "\t4\n",
      "\t5\n",
      "key\n",
      "\t1\n",
      "\t2\n",
      "这个在存取读入的文件时会很有用的，下面的实战练习会用到这个。\n"
     ]
    }
   ],
   "source": [
    "print \"字典的value可以是一个列表\"\n",
    "a = 'key'\n",
    "b = 'key2'\n",
    "aDict = {}\n",
    "print aDict\n",
    "aDict[a] = []\n",
    "print aDict\n",
    "aDict[a].append(1)\n",
    "aDict[a].append(2)\n",
    "print aDict\n",
    "aDict[b] = [3,4,5]\n",
    "\n",
    "print\n",
    "for key, subL in aDict.items():\n",
    "    print key\n",
    "    for item in subL:\n",
    "        print \"\\t%s\" % item\n",
    "\n",
    "print \"这个在存取读入的文件时会很有用的，下面的实战练习会用到这个。\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "事理因人言而悟者，有悟还有迷，总不如自悟之了了；意兴从外境而得者，有得还有失，总不如自得之休休。\n",
      "书不如思贵，意不可言传\n"
     ]
    }
   ],
   "source": [
    "aDict = {'菜根谭':'事理因人言而悟者，有悟还有迷，总不如自悟之了了；意兴从外境而得者，有得还有失，总不如自得之休休。'}\n",
    "print aDict['菜根谭']\n",
    "aDict['庄子'] = '书不如思贵，意不可言传'\n",
    "print aDict['庄子'] "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "字典的value也可以是字典\n",
      "{}\n",
      "{'key': {}}\n",
      "{'key': {'subkey': 'subvalue'}}\n",
      "\n",
      "key2\n",
      "\t1\t2\n",
      "\t3\t4\n",
      "key\n",
      "\tsubkey\tsubvalue\n",
      "\n",
      "这个在存取读入的文件时会很有用的，下面的实战练习会用到这个。\n"
     ]
    }
   ],
   "source": [
    "print \"字典的value也可以是字典\"\n",
    "a = 'key'\n",
    "b = 'key2'\n",
    "aDict = {}\n",
    "print aDict\n",
    "aDict[a] = {}\n",
    "print aDict\n",
    "aDict[a]['subkey'] = 'subvalue'\n",
    "print aDict\n",
    "aDict[b] = {1:2,3:4}\n",
    "\n",
    "#aDict[(a,b)] = 2\n",
    "#aDict['a'] = 2\n",
    "#aDict['b'] = 2\n",
    "\n",
    "print\n",
    "for key, subD in aDict.items():\n",
    "    print key\n",
    "    for subKey, subV in subD.items():\n",
    "        print \"\\t%s\\t%s\" % (subKey, subV)\n",
    "\n",
    "print \"\\n这个在存取读入的文件时会很有用的，下面的实战练习会用到这个。\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 输入输出"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 交互式输入输出"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "在很多时候，你会想要让你的程序与用户（可能是你自己）交互。你会从用户那里得到输入，然后打印一些结果。我们可以分别使用raw_input和print语句来完成这些功能。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Please input a string\n",
      "> a\n",
      "The string you typed in is:  a\n"
     ]
    }
   ],
   "source": [
    "a = raw_input(\"Please input a string\\n> \")\n",
    "\n",
    "print \"The string you typed in is: \", a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "这是一个保留例子，仅供玩耍\n",
      "\n",
      "Please input a number to check if you are lucky enough to guess right: \n",
      "5\n",
      "\n",
      "Your are so smart!!! ^_^ ^_^\n",
      "Please input a number to check if you are lucky enough to guess right: \n",
      "7\n",
      "\n",
      "Sorry, but you are not right. %>_<%\n",
      "Do you want to try again? [Y/N] \n",
      "Y\n",
      "\n",
      "Here comes another run. Enjoy!\n",
      "\n",
      "Please input a number to check if you are lucky enough to guess right: \n",
      "8\n",
      "\n",
      "Sorry, but you are not right. %>_<%\n",
      "Do you want to try again? [Y/N] \n",
      "N\n",
      "\n",
      "Bye-bye\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print \"这是一个保留例子，仅供玩耍\\n\"\n",
    "lucky_num = 5\n",
    "c = 0\n",
    "\n",
    "while True:\n",
    "    b = int(raw_input(\"Please input a number to check if you are \\\n",
    "lucky enough to guess right: \\n\"))\n",
    "    if b == lucky_num:\n",
    "        print \"\\nYour are so smart!!! ^_^ ^_^\"\n",
    "        #----------------------------------------------------\n",
    "    #--------------------------------------------------------\n",
    "    else:\n",
    "        print \"\\nSorry, but you are not right. %>_<%\"\n",
    "        while 1:\n",
    "            c = raw_input(\"Do you want to try again? [Y/N] \\n\")\n",
    "            if c == 'Y':\n",
    "                try_again = 1\n",
    "                break                \n",
    "            elif c == 'N':\n",
    "                try_again = 0\n",
    "                break\n",
    "            else:\n",
    "                print \"I can not understand you, please check your input. \\n\"\n",
    "                continue\n",
    "        #----------------------------------------------------\n",
    "        if try_again:\n",
    "            print \"\\nHere comes another run. Enjoy!\\n\"\n",
    "            continue\n",
    "        else:\n",
    "            print \"\\nBye-bye\\n\"\n",
    "            break"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 文件读写"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "文件读写是最常见的输入和输出操作。你可以实用`file`或`open`来实现。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "新建一个文件\n",
      "以写入模式(w)打开一个文件并命名为(Test_file.txt)\n"
     ]
    }
   ],
   "source": [
    "print \"新建一个文件\"\n",
    "\n",
    "context = '''The best way to learn python contains two steps:\n",
    "1. Rember basic things mentionded here masterly.\n",
    "\n",
    "2. Practise with real demands.\n",
    "'''\n",
    "\n",
    "print \"以写入模式(w)打开一个文件并命名为(Test_file.txt)\"\n",
    "fh = open(\"Test_file.txt\",\"w\") \n",
    "print >>fh, context\n",
    "#fh.write(context)\n",
    "fh.close() #文件操作完成后必须关闭文件句柄"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "以只读模式(r)读入一个名为(Test_file.txt)的文件\n",
      "\n",
      "The best way to learn python contains two steps:\n",
      "\n",
      "1. Rember basic things mentionded here masterly.\n",
      "\n",
      "\n",
      "\n",
      "2. Practise with real demands.\n",
      "\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print \"以只读模式(r)读入一个名为(Test_file.txt)的文件\"\n",
    "\n",
    "print\n",
    "\n",
    "for line in open(\"Test_file.txt\"):\n",
    "    print line"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "避免中间空行的输出。\n",
      "从文件中读取的每一行都带有一个换行符，\n",
      "而Python的print默认会在输出结束时加上换行符，\n",
      "因此打印一行会空出一行。为了解决这个问题，有下面两套方案。\n",
      "在print语句后加上逗号(,)可以阻止Python对每次输出自动添加的换行符\n",
      "\n",
      "The best way to learn python contains two steps:\n",
      "1. Rember basic things mentionded here masterly.\n",
      "\n",
      "2. Practise with real demands.\n",
      "\n",
      "\n",
      "去掉每行自身的换行符\n",
      "The best way to learn python contains two steps:\n",
      "1. Rember basic things mentionded here masterly.\n",
      "\n",
      "2. Practise with real demands.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print '''避免中间空行的输出。\n",
    "从文件中读取的每一行都带有一个换行符，\n",
    "而Python的print默认会在输出结束时加上换行符，\n",
    "因此打印一行会空出一行。为了解决这个问题，有下面两套方案。'''\n",
    "\n",
    "print \"在print语句后加上逗号(,)可以阻止Python对每次输出自动添加的换行符\"\n",
    "print\n",
    "\n",
    "for line in open(\"Test_file.txt\"):\n",
    "    print line,\n",
    "\n",
    "print\n",
    "\n",
    "print \"去掉每行自身的换行符\"\n",
    "for line in open(\"Test_file.txt\"):\n",
    "    print line.strip()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 实战练习（一）"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 背景知识"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**1. FASTA文件格式**\n",
    "\n",
    "> \\>seq_name_1\n",
    "\n",
    "> sequence1\n",
    "\n",
    "> \\>seq_name_2\n",
    "\n",
    ">sequence2\n",
    "\n",
    "**2. FASTQ文件格式**\n",
    "\n",
    "> @HWI-ST1223:80:D1FMTACXX:2:1101:1243:2213 1:N:0:AGTCAA\n",
    "\n",
    "> TCTGTGTAGCCNTGGCTGTCCTGGAACTCACTTTGTAGACCAGGCTGGCATGCA\n",
    "\n",
    "> +\n",
    "\n",
    "> BCCFFFFFFHH#4AFHIJJJJJJJJJJJJJJJJJIJIJJJJJGHIJJJJJJJJJ\n",
    "\n",
    "<img src=\"Image/fastq_format.png\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 作业 (一)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. 给定FASTA格式的文件(test1.fa 和 test2.fa)，写一个程序 `cat.py` 读入文件，并输出到屏幕\n",
    "    * 用到的知识点\n",
    "        * open(file)\n",
    "        * for .. in loop\n",
    "        * print\n",
    "        * the amazng , or strip() function\n",
    "2. 给定FASTQ格式的文件(test1.fq), 写一个程序 `cat.py` 读入文件，并输出到屏幕\n",
    "    * 用到的知识点\n",
    "        * 同上\n",
    "3. 写程序 `splitName.py`, 读入test2.fa, 并取原始序列名字第一个空格前的名字为处理后的序列名字，输出到屏幕\n",
    "    * 用到的知识点\n",
    "        * split\n",
    "        * 字符串的索引\n",
    "    * 输出格式为：\n",
    "```\n",
    ">NM_001011874\n",
    "gcggcggcgggcgagcgggcgctggagtaggagctg.......\n",
    "```\n",
    "\n",
    "4. 写程序 `formatFasta.py`, 读入test2.fa，把每条FASTA序列连成一行然后输出\n",
    "    * 用到的知识点\n",
    "        * join\n",
    "        * strip    \n",
    "    * 输出格式为:\n",
    "```\n",
    "    >NM_001011874\n",
    "    gcggcggcgggc......TCCGCTG......GCGTTCACC......CGGGGTCCGGAG\n",
    "```\n",
    "4. 写程序 `formatFasta-2.py`, 读入test2.fa，把每条FASTA序列分割成80个字母一行的序列\n",
    "    * 用到的知识点\n",
    "        * 字符串切片操作\n",
    "        * range\n",
    "    * 输出格式为\n",
    "```    \n",
    "    >NM_001011874\n",
    "    gcggcggcgc.(60个字母).TCCGCTGACG #(每行80个字母)\n",
    "    acgtgctacg.(60个字母).GCGTTCACCC\n",
    "    ACGTACGATG(最后一行可不足80个字母)\n",
    "```     \n",
    "4. 写程序 `sortFasta.py`, 读入test2.fa, 并取原始序列名字第一个空格前的名字为处理后的序列名字，排序后输出\n",
    "    * 用到的知识点\n",
    "        * sort\n",
    "        * dict\n",
    "        * aDict[key] = []\n",
    "        * aDict[key].append(value)\n",
    "        \n",
    "5. 提取给定名字的序列\n",
    "    * 写程序 `grepFasta.py`, 提取fasta.name中名字对应的test2.fa的序列，并输出到屏幕。\n",
    "    * 写程序 `grepFastq.py`, 提取fastq.name中名字对应的test1.fq的序列，并输出到文件。\n",
    "        * 用到的知识点\n",
    "            * print >>fh, or fh.write()\n",
    "            * 取模运算，4 % 2 == 0\n",
    "    \n",
    "6. 写程序 `screenResult.py`, 筛选test.expr中foldChange大于2的基因并且padj小于0.05的基，可以输出整行或只输出基因名字\n",
    "    * 用到的知识点\n",
    "        * 逻辑与操作符 and \n",
    "        * 文件中读取的内容都为字符串，需要用int转换为整数，float转换为浮点数\n",
    "\n",
    "6. 写程序 `transferMultipleColumToMatrix.py` 将文件(multipleColExpr.txt)中基因在多个组织中的表达数据转换为矩阵形式\n",
    "    * 用到的知识点\n",
    "        * aDict['key'] = {}\n",
    "        * aDict['key']['key2'] = value\n",
    "        * if key not in aDict\n",
    "        * aDict = {'ENSG00000000003': {\"A-431\": 21.3, \"A-549\", 32.5,...},\"ENSG00000000003\":{},}\n",
    "    * 输入格式(只需要前3列就可以)\n",
    "```\n",
    "Gene    Sample  Value   Unit    Abundance\n",
    "ENSG00000000003 A-431   21.3    FPKM    Medium\n",
    "ENSG00000000003 A-549   32.5    FPKM    Medium\n",
    "ENSG00000000003 AN3-CA  38.2    FPKM    Medium\n",
    "ENSG00000000003 BEWO    31.4    FPKM    Medium\n",
    "ENSG00000000003 CACO-2  63.9    FPKM    High\n",
    "ENSG00000000005 A-431   0.0     FPKM    Not detected\n",
    "ENSG00000000005 A-549   0.0     FPKM    Not detected\n",
    "ENSG00000000005 AN3-CA  0.0     FPKM    Not detected\n",
    "ENSG00000000005 BEWO    0.0     FPKM    Not detected\n",
    "ENSG00000000005 CACO-2  0.0     FPKM    Not detected\n",
    "```\n",
    "    * 输出格式\n",
    "```\n",
    "Name\tA-431\tA-549\tAN3-CA\tBEWO\tCACO-2\n",
    "ENSG00000000460\t25.2\t14.2\t10.6\t24.4\t14.2\n",
    "ENSG00000000938\t0.0\t0.0\t0.0\t0.0\t0.0\n",
    "ENSG00000001084\t19.1\t155.1\t24.4\t12.6\t23.5\n",
    "ENSG00000000457\t2.8\t3.4\t3.8\t5.8\t2.9\n",
    "```\n",
    "\n",
    "6. 写程序 `reverseComplementary.py`计算序列 `ACGTACGTACGTCACGTCAGCTAGAC`的反向互补序列\n",
    "    * 用到的知识点\n",
    "        * reverse\n",
    "        * list(seq)\n",
    "7. 写程序 `collapsemiRNAreads.py`转换smRNA-Seq的测序数据\n",
    "    * 输入文件格式(mir.collapse, tab-分割的两列文件，第一列为序列，第二列为序列被测到的次数)\n",
    "```\n",
    "        ID_REF        VALUE\n",
    "        ACTGCCCTAAGTGCTCCTTCTGGC        2\n",
    "        ATAAGGTGCATCTAGTGCAGATA        25\n",
    "        TGAGGTAGTAGTTTGTGCTGTTT        100\n",
    "        TCCTACGAGTTGCATGGATTC        4\n",
    "```\n",
    "    * 输出文件格式 (mir.collapse.fa, 名字的前3个字母为样品的特异标示，中间的数字表示第几条序列，是序列名字的唯一标示，第三部分是x加每个reads被测到的次数。三部分用下划线连起来作为fasta序列的名字。)\n",
    "```        \n",
    "        >ESB_1_x2\n",
    "        ACTGCCCTAAGTGCTCCTTCTGGC\n",
    "        >ESB_2_x25\n",
    "        ATAAGGTGCATCTAGTGCAGATA\n",
    "        >ESB_3_x100\n",
    "        TGAGGTAGTAGTTTGTGCTGTTT\n",
    "        >ESB_4_x4\n",
    "        TCCTACGAGTTGCATGGATTC\n",
    "```\n",
    "8. 简化的短序列匹配程序 (map.py) 把short.fa中的序列比对到ref.fa, 输出短序列匹配到ref.fa文件中哪些序列的哪些位置\n",
    "    * 用到的知识点\n",
    "        * find\n",
    "    * 输出格式 (输出格式为bed格式，第一列为匹配到的染色体，第二列和第三列为匹配到染色体序列的起始终止位置（位置标记以0为起始，代表第一个位置；终止位置不包含在内，第一个例子中所示序列的位置是(199,208](前闭后开，实际是chr1染色体第199-206的序列，0起始). 第4列为短序列自身的序列.)。\n",
    "    * 附加要求：可以只匹配到给定的模板链，也可以考虑匹配到模板链的互补链。这时第5列可以为短序列的名字，第六列为链的信息，匹配到模板链为'+'，匹配到互补链为'-'。注意匹配到互补链时起始位置也是从模板链的5'端算起的。\n",
    "``` \n",
    "    chr1\t199\t208\tTGGCGTTCA\n",
    "    chr1\t207\t216\tACCCCGCTG\n",
    "    chr2\t63\t70\tAAATTGC\n",
    "    chr3\t0\t7\tAATAAAT\n",
    "```\n",
    "\n",
    "10. 备注：\n",
    "    * 每个提到提到的“用到的知识点”为相对于前面的题目新增的知识点，请综合考虑。此外，对于不同的思路并不是所有提到的知识点都会用着，而且也可能会用到未提到的知识点。但是所有知识点都在前面的讲义部分有介绍。\n",
    "    * 每个程序对于你身边会写的人来说都很简单，因此你一定要克制住，独立去把答案做出，多看错误提示，多比对程序输出结果和预期结果的差异。\n",
    "    * 学习锻炼“读程序”，即对着文件模拟整个的读入、处理过程来发现可能的逻辑问题。\n",
    "    * 程序运行没有错误不代表你写的程序完成了你的需求，你要去查验输出结果是不是你想要的。\n",
    "11. 关于程序调试\n",
    "    * 在初写程序时，可能会出现各种各样的错误，常见的有缩进不一致，变量名字拼写错误，丢失冒号，文件名未加引号等，这时要根据错误提示查看错误类型是什么，出错的是哪一行来定位错误。当然，有的时候报错的行自身不一定有错，可能是其前面或后面的行出现了错误。\n",
    "    * **用脑袋运行程序**：当程序写作完成后，自己尝试对着数据文件，一行一行的执行程序，来看程序的运行是否与自己想干的活一致，有没有纰漏。\n",
    "    * 当结果不符合预期时，要学会**使用print来查看每步的操作是否正确**，比如我读入了字典，我就打印下字典，看看读入的是不是我想要的，是否含有不该存在的字符；或者**在每个判断句、函数调入的情况下打印个字符，来跟踪程序的运行轨迹**。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 函数操作"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "函数是重用的程序段。它们允许你给一块语句一个名称，然后你可以在你的程序的任何地方使用这个名称任意多次地运行这个语句块。这被称为 `调用` 函数。我们已经使用了许多内建的函数，比如`len`和`range`。\n",
    "\n",
    "函数通过`def`关键字定义。`def`关键字后跟一个函数的 `标识符` 名称，然后跟一对圆括号。圆括号之中可以包括一些变量名，该行以冒号结尾。接下来是一块语句，它们是函数体。\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Hello, you!\n"
     ]
    }
   ],
   "source": [
    "def print_hello():\n",
    "    print \"Hello, you!\"\n",
    "\n",
    "print_hello()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Hello, you!\n",
      "Hello, me!\n"
     ]
    }
   ],
   "source": [
    "def hello(who):\n",
    "    print \"Hello, %s!\" % who\n",
    "\n",
    "hello('you')\n",
    "hello('me')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "把之前写过的语句块稍微包装下就是函数了\n",
      "\n",
      "[1, 9, 13, 15, 17]\n",
      "[2, 12]\n"
     ]
    }
   ],
   "source": [
    "print \"把之前写过的语句块稍微包装下就是函数了\\n\"\n",
    "\n",
    "def findAll(string, pattern):\n",
    "    posL = []\n",
    "    pos = 0\n",
    "    for i in string:\n",
    "        pos += 1\n",
    "        if i == pattern:\n",
    "            posL.append(pos)\n",
    "    #-------------------\n",
    "    return posL\n",
    "#------END of findAll-------\n",
    "a = findAll(\"ABCDEFDEACFBACACA\", \"A\")\n",
    "print a \n",
    "print findAll(\"ABCDEFDEACFBACACA\", \"B\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'>NM_0112835 gene=Rp15 CDS=128-6412': 'AATAAATCCAAAGACATTTGTTTACGTGAAACAAGCAGGTTGCATATCCAGTGACGTTTATACAGACCAC', '>NM_011283 gene=Rp1 CDS=128-6412': 'AATAAATCCAAAGACATTTGTTTACGTGAAACAAGCAGGTTGCATATCCAGTGACGTTTATACAGACCAC', '>NM_001011874 gene=Xkr4 CDS=151-2091': 'gcggcggcgggcgagcgggcgctggagtaggagctggggagcggcgcggccggggaaggaagccagggcg', '>NM_001195662 gene=Rp1 CDS=55-909': 'AGGTCTCACCCAAAATGAGTGACACACCTTCTACTAGTTTCTCCATGATTCATCTGACTTCTGAAGGTCA'}\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'>NM_001011874 gene=Xkr4 CDS=151-2091': 'gcggcggcgggcgagcgggcgctggagtaggagctggggagcggcgcggccggggaaggaagccagggcgaggcgaggaggtggcgggaggaggagacagcagggacaggTGTCAGATAAAGGAGTGCTCTCCTCCGCTGCCGAGGCATCATGGCCGCTAAGTCAGACGGGAGGCTGAAGATGAAGAAGAGCAGCGACGTGGCGTTCACCCCGCTGCAGAACTCGGACAATTCGGGCTCTGTGCAAGGACTGGCTCCAGGCTTGCCGTCGGGGTCCGGAG',\n",
       " '>NM_001195662 gene=Rp1 CDS=55-909': 'AAGCTCAGCCTTTGCTCAGATTCTCCTCTTGATGAAACAAAGGGATTTCTGCACATGCTTGAGAAATTGCAGGTCTCACCCAAAATGAGTGACACACCTTCTACTAGTTTCTCCATGATTCATCTGACTTCTGAAGGTCAAGTTCCTTCCCCTCGCCATTCAAATATCACTCATCCTGTAGTGGCTAAACGCATCAGTTTCTATAAGAGTGGAGACCCACAGTTTGGCGGCGTTCGGGTGGTGGTCAACCCTCGTTCCTTTAAGACTTTTGACGCTCTGCTGGACAGTTTATCCAGGAAGGTACCCCTGCCCTTTGGGGTAAGGAACATCAGCACGCCCCGTGGACGACACAGCATCACCAGGCTGGAGGAGCTAGAGGACGGCAAGTCTTATGTGTGCTCCCACAATAAGAAGGTGCTG',\n",
       " '>NM_011283 gene=Rp1 CDS=128-6412': 'AATAAATCCAAAGACATTTGTTTACGTGAAACAAGCAGGTTGCATATCCAGTGACGTTTATACAGACCACACAAACTATTTACTCTTTTCTTCGTAAGGAAAGGTTCAACTTCTGGTCTCACCCAAAATGAGTGACACACCTTCTACTAGTTTCTCCATGATTCATCTGACTTCTGAAGGTCAAGTTCCTTCCCCTCGCCATTCAAATATCACTCATCCTGTAGTGGCTAAACGCATCAGTTTCTATAAGAGTGGAGACCCACAGTTTGGCGGCGTTCGGGTGGTGGTCAACCCTCGTTCCTTTAAGACTTTTGACGCTCTGCTGGACAGTTTATCCAGGAAGGTACCCCTGCCCTTTGGGGTAAGGAACATCAGCACGCCCCGTGGACGACACAGCATCACCAGGCTGGAGGAGCTAGAGGACGGCAAGTCTTATGTGTGCTCCCACAATAAGAAGGTGCTGCCAGTTGACCTGGACAAGGCCCGCAGGCGCCCTCGGCCCTGGCTGAGTAGTCGCTCCATAAGCACGCATGTGCAGCTCTGTCCTGCAACTGCCAATATGTCCACCATGGCACCTGGCATGCTCCGTGCCCCAAGGAGGCTCGTGGTCTTCCGGAATGGTGACCCGAA',\n",
       " '>NM_0112835 gene=Rp1 CDS=128-6412': 'AATAAATCCAAAGACATTTGTTTACGTGAAACAAGCAGGTTGCATATCCAGTGACGTTTATACAGACCACACAAACTATTTACTCTTTTCTTCGTAAGGAAAGGTTCAACTTCTGGTCTCACCCAAAATGAGTGACACACCTTCTACTAGTTTCTCCATGATTCATCTGACTTCTGAAGGTCAAGTTCCTTCCCCTCGCCATTCAAATATCACTCATCCTGTAGTGGCTAAACGCATCAGTTTCTATAAGAGTGGAGACCCACAGTTTGGCGGCGTTCGGGTGGTGGTCAACCCTCGTTCCTTTAAGACTTTTGACGCTCTGCTGGACAGTTTATCCAGGAAGGTACCCCTGCCCTTTGGGGTAAGGAACATCAGCACGCCCCGTGGACGACACAGCATCACCAGGCTGGAGGAGCTAGAGGACGGCAAGTCTTATGTGTGCTCCCACAATAAGAAGGTGCTGCCAGTTGACCTGGACAAGGCCCGCAGGCGCCCTCGGCCCTGGCTGAGTAGTCGCTCCATAAGCACGCATGTGCAGCTCTGTCCTGCAACTGCCAATATGTCCACCATGGCACCTGGCATGCTCCGTGCCCCAAGGAGGCTCGTGGTCTTCCGGAATGGTGACCCGAA'}"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def read(file):\n",
    "    aDict = {}\n",
    "    for line in open(file):\n",
    "        if line[0] == '>':\n",
    "            name = line.strip()\n",
    "            aDict[name] = []\n",
    "        else:\n",
    "            aDict[name].append(line.strip())\n",
    "    #----------------------------------\n",
    "    for name, lineL in aDict.items():\n",
    "        aDict[name] = ''.join(lineL)\n",
    "    return aDict\n",
    "\n",
    "print read(\"data/test1.fa\")\n",
    "read(\"data/test2.fa\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 作业（二）"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "6. 将 “作业（一）” 中的程序块用函数的方式重写，并调用执行\n",
    "    * 用到的知识点\n",
    "        * def func(para1,para2,...):\n",
    "        * func(para1,para2,...)\n",
    "10. 备注：\n",
    "    * 每个提到提到的“用到的知识点”为相对于前面的题目新增的知识点，请综合考虑。此外，对于不同的思路并不是所有提到的知识点都会用着，而且也可能会用到未提到的知识点。但是所有知识点都在前面的讲义部分有介绍。\n",
    "    * 每个程序对于你身边会写的人来说都很简单，因此你一定要克制住，独立去把答案做出，多看错误提示，多比对程序输出结果和预期结果的差异。\n",
    "    * 学习锻炼“读程序”，即对着文件模拟整个的读入、处理过程来发现可能的逻辑问题。\n",
    "    * 程序运行没有错误不代表你写的程序完成了你的需求，你要去插眼输出结果是不是你想要的。\n",
    "11. 关于程序调试\n",
    "    * 在初写程序时，可能会出现各种各样的错误，常见的有缩进不一致，变量名字拼写错误，丢失冒号，文件名未加引号等，这时要根据错误提示查看错误类型是什么，出错的是哪一行来定位错误。当然，有的时候报错的行自身不一定有错，可能是其前面或后面的行出现了错误。\n",
    "    * 当结果不符合预期时，要学会使用print来查看每步的操作是否正确，比如我读入了字典，我就打印下字典，看看读入的是不是我想要的，是否含有不该存在的字符；或者在每个判断句、函数调入的情况下打印个字符，来跟踪程序的运行轨迹。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 模块"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Python内置了很多标准库，如做数学运算的 `math`, 调用系统功能的 `sys`, 处理正则表达式的 `re`, 操作系统相关功能的 `os`等。我们主要关注两个库:\n",
    "* sys\n",
    "    * sys.argv 处理命令行参数\n",
    "    * sys.exit() 退出函数\n",
    "    * sys.stdin 标准输入\n",
    "    * sys.stderr 标准错误\n",
    "* os\n",
    "    * os.system()或os.popen() 执行系统命令\n",
    "    * os.getcwd() 获取当前目录\n",
    "    * os.remove() 删除文件"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'D:\\\\project\\\\github\\\\PBR_training'"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import os\n",
    "os.getcwd()\n",
    "#help(os.getcwd)\n",
    "#os.remove(r'D:\\project\\github\\PBR_training\\script\\splitName.py')\n",
    "#os.system('rm file')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'D:\\\\project\\\\github\\\\PBR_training'"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from os import getcwd\n",
    "getcwd()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 命令行参数"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`sys.argv`是一个列表，存储了包含程序名字在内的传给程序的命令行参数。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Writing testSys.py\n"
     ]
    }
   ],
   "source": [
    "%%writefile testSys.py\n",
    "import sys\n",
    "print sys.argv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['testSys.py', \"'abc'\", '1']\n"
     ]
    }
   ],
   "source": [
    "%run testSys 'abc' 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Writing cat.py\n"
     ]
    }
   ],
   "source": [
    "%%writefile cat.py\n",
    "import sys\n",
    "\n",
    "def read_print_file(filename):\n",
    "    for line in open(filename):\n",
    "        print line,\n",
    "#------END read_print_file--------------------------\n",
    "\n",
    "#main函数及其调用部分是我个人写程序的固定格式，照搬就可以\n",
    "def main(): #一般主程序会包含在main函数中，在文件的最后调用main函数即可运行程序\n",
    "    if len(sys.argv) < 2:  #如果命令行参数不足两个，则提示操作\n",
    "        #一般提示信息输出到标准错误\n",
    "        print >>sys.stderr, \"Usage: python %s filename\" % sys.argv[0] \n",
    "        sys.exit(0)\n",
    "    file = sys.argv[1]\n",
    "    read_print_file(file)\n",
    "#--------END main------------------\n",
    "#这句话是说只有在文件被执行时才调用main函数。如果这个文件被其它文件调用，则不执行main函数。\n",
    "if __name__ == '__main__': \n",
    "    main()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Usage: python cat.py filename\n"
     ]
    }
   ],
   "source": [
    "%run cat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ">NM_001011874 gene=Xkr4 CDS=151-2091\n",
      "gcggcggcgggcgagcgggcgctggagtaggagctggggagcggcgcggccggggaaggaagccagggcg\n",
      ">NM_001195662 gene=Rp1 CDS=55-909\n",
      "AGGTCTCACCCAAAATGAGTGACACACCTTCTACTAGTTTCTCCATGATTCATCTGACTTCTGAAGGTCA\n",
      ">NM_0112835 gene=Rp15 CDS=128-6412\n",
      "AATAAATCCAAAGACATTTGTTTACGTGAAACAAGCAGGTTGCATATCCAGTGACGTTTATACAGACCAC\n",
      ">NM_011283 gene=Rp1 CDS=128-6412\n",
      "AATAAATCCAAAGACATTTGTTTACGTGAAACAAGCAGGTTGCATATCCAGTGACGTTTATACAGACCAC\n"
     ]
    }
   ],
   "source": [
    "%run cat data/test1.fa"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "使用 `optparse`，功能更强大 (保留内容)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Overwriting skeleton.py\n"
     ]
    }
   ],
   "source": [
    "%%writefile skeleton.py\n",
    "#!/usr/bin/env python\n",
    "\n",
    "desc = '''\n",
    "Functional description:\n",
    "\n",
    "'''\n",
    "\n",
    "import sys\n",
    "import os\n",
    "from time import localtime, strftime \n",
    "timeformat = \"%Y-%m-%d %H:%M:%S\"\n",
    "from optparse import OptionParser as OP\n",
    "\n",
    "def cmdparameter(argv):\n",
    "    if len(argv) == 1:\n",
    "        global desc\n",
    "        print >>sys.stderr, desc\n",
    "        cmd = 'python ' + argv[0] + ' -h'\n",
    "        os.system(cmd)\n",
    "        sys.exit(0)\n",
    "    usages = \"%prog -i file\"\n",
    "    parser = OP(usage=usages)\n",
    "    parser.add_option(\"-i\", \"--input-file\", dest=\"filein\",\n",
    "        metavar=\"FILEIN\", help=\"The name of input file. \\\n",
    "Standard input is accepted.\")\n",
    "    parser.add_option(\"-v\", \"--verbose\", dest=\"verbose\",\n",
    "        default=0, help=\"Show process information\")\n",
    "    parser.add_option(\"-d\", \"--debug\", dest=\"debug\",\n",
    "        default=False, help=\"Debug the program\")\n",
    "    (options, args) = parser.parse_args(argv[1:])\n",
    "    assert options.filein != None, \"A filename needed for -i\"\n",
    "    return (options, args)\n",
    "#--------------------------------------------------------------------\n",
    "\n",
    "\n",
    "def main():\n",
    "    options, args = cmdparameter(sys.argv)\n",
    "    #-----------------------------------\n",
    "    file = options.filein\n",
    "    verbose = options.verbose\n",
    "    debug = options.debug\n",
    "    #-----------------------------------\n",
    "    if file == '-':\n",
    "        fh = sys.stdin\n",
    "    else:\n",
    "        fh = open(file)\n",
    "    #--------------------------------\n",
    "    for line in fh:\n",
    "        pass\n",
    "    #-------------END reading file----------\n",
    "    #----close file handle for files-----\n",
    "    if file != '-':\n",
    "        fh.close()\n",
    "    #-----------end close fh-----------\n",
    "    if verbose:\n",
    "        print >>sys.stderr,\\\n",
    "            \"--Successful %s\" % strftime(timeformat, localtime())\n",
    "if __name__ == '__main__':\n",
    "    startTime = strftime(timeformat, localtime())\n",
    "    main()\n",
    "    endTime = strftime(timeformat, localtime())\n",
    "    fh = open('python.log', 'a')\n",
    "    print >>fh, \"%s\\n\\tRun time : %s - %s \" % \\\n",
    "        (' '.join(sys.argv), startTime, endTime)\n",
    "    fh.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Usage: skeleton.py -i file\n",
      "\n",
      "Options:\n",
      "  -h, --help            show this help message and exit\n",
      "  -i FILEIN, --input-file=FILEIN\n",
      "                        The name of input file. Standard input is accepted.\n",
      "  -v VERBOSE, --verbose=VERBOSE\n",
      "                        Show process information\n",
      "  -d DEBUG, --debug=DEBUG\n",
      "                        Debug the program\n"
     ]
    }
   ],
   "source": [
    "%run skeleton -h"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 作业（三）"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "7. 使 “作业（二）” 中的程序都能接受命令行参数\n",
    "    * 用到的知识点\n",
    "        * import sys\n",
    "        * sys.argv\n",
    "        * import optparse\n",
    "        \n",
    "10. 备注\n",
    "    * 每个提到提到的“用到的知识点”为相对于前面的题目新增的知识点，请综合考虑。此外，对于不同的思路并不是所有提到的知识点都会用着，而且也可能会用到未提到的知识点。但是所有知识点都在前面的讲义部分有介绍。\n",
    "    * 每个程序对于你身边会写的人来说都很简单，因此你一定要克制住，独立去把答案做出，多看错误提示，多比对程序输出结果和预期结果的差异。\n",
    "    * 学习锻炼“读程序”，即对着文件模拟整个的读入、处理过程来发现可能的逻辑问题。\n",
    "    * 程序运行没有错误不代表你写的程序完成了你的需求，你要去插眼输出结果是不是你想要的。\n",
    "11. 关于程序调试\n",
    "    * 在初写程序时，可能会出现各种各样的错误，常见的有缩进不一致，变量名字拼写错误，丢失冒号，文件名未加引号等，这时要根据错误提示查看错误类型是什么，出错的是哪一行来定位错误。当然，有的时候报错的行自身不一定有错，可能是其前面或后面的行出现了错误。\n",
    "    * 当结果不符合预期时，要学会使用print来查看每步的操作是否正确，比如我读入了字典，我就打印下字典，看看读入的是不是我想要的，是否含有不该存在的字符；或者在每个判断句、函数调入的情况下打印个字符，来跟踪程序的运行轨迹。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 更多Python内容"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id=\"单语句块\"></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## **单语句块**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "yes\n",
      "yes\n",
      "3\n",
      "3\n",
      "5\n"
     ]
    }
   ],
   "source": [
    "if True:\n",
    "    print 'yes'\n",
    "    \n",
    "if True: print 'yes'\n",
    "\n",
    "x = 5\n",
    "y = 3\n",
    "\n",
    "if x > y:\n",
    "    print y\n",
    "else:\n",
    "    print x\n",
    "#-------------\n",
    "print y if y < x else x \n",
    "print x"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id=\"列表综合\"></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## **列表综合，生成新列表的简化的for循环**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2, 4, 6, 8, 10]\n"
     ]
    }
   ],
   "source": [
    "aList = [1,2,3,4,5]\n",
    "bList = []\n",
    "for i in aList:\n",
    "    bList.append(i * 2)\n",
    "#-----------------------------------\n",
    "#nameL = [line.strip() for line in open(file)]\n",
    "    \n",
    "bList = [i * 2 for i in aList]\n",
    "print bList"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "列表综合可以做判断的\n",
      "[2, 6, 10]\n"
     ]
    }
   ],
   "source": [
    "print \"列表综合可以做判断的\"\n",
    "aList = [1,2,3,4,5]\n",
    "bList = [i * 2 for i in aList if i%2 != 0]\n",
    "print bList"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "列表综合也可以嵌套的\n",
      "[5, 4, 3, 2, 1, 10, 8, 6, 4, 2, 15, 12, 9, 6, 3, 20, 16, 12, 8, 4, 25, 20, 15, 10, 5]\n"
     ]
    }
   ],
   "source": [
    "print \"列表综合也可以嵌套的\"\n",
    "aList = [1,2,3,4,5]\n",
    "bList = [5,4,3,2,1]\n",
    "bList = [i * j for i in aList for j in bList]\n",
    "\n",
    "#for i in aList:\n",
    "#    for j in bList:\n",
    "#        print i * j\n",
    "print bList"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "断言，设定运行过程中必须满足的条件，当情况超出预期时报错。常用于文件读入或格式判断时，有助于预防异常的读入或操作。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "AssertionError",
     "evalue": "a is 1, b is 2",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[1;31mAssertionError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-94-1cdfb2fe6c3a>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[0ma\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      2\u001b[0m \u001b[0mb\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;36m2\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 3\u001b[1;33m \u001b[1;32massert\u001b[0m \u001b[0ma\u001b[0m \u001b[1;33m==\u001b[0m \u001b[0mb\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m\"a is %s, b is %s\"\u001b[0m \u001b[1;33m%\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0ma\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mb\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      4\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0ma\u001b[0m \u001b[1;33m==\u001b[0m \u001b[0mb\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mAssertionError\u001b[0m: a is 1, b is 2"
     ]
    }
   ],
   "source": [
    "a = 1\n",
    "b = 2\n",
    "assert a == b, \"a is %s, b is %s\" % (a, b)\n",
    "\n",
    "if a == b:\n",
    "    pass\n",
    "else:\n",
    "    print \"a is %s, b is %s\" % (a, b)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id=\"lmfr\"></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## lambda, map, filer, reduce (保留节目)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* lambda产生一个没有名字的函数，通常为了满足一次使用，其使用语法为`lambda argument_list: expression`。参数列表是用逗号分隔开的一个列表，表达式是这些参数的组合操作。 \n",
    "* map执行一个循环操作，使用语法为`map(func, seq)`。第一个参数是要调用的函数或函数的名字，第二个参数是一个序列（如列表、字符串、字典）。map会以序列的每个元素为参数调用func，并新建一个输出列表。\n",
    "* filter用于过滤列表，使用语法为`filter(func, list)`。以第二个参数的每个元素调用func，返回值为True则保留，否则舍弃。\n",
    "* reduce连续对列表的元素应用函数，使用语法为`reduce(func, list)`。如果我们有一个列表aList = [1,2,3, ... ,n ], 调用`reduce(func, aList)`后进行的操作为: 首先前两个元素会传入函数func做运算，返回值替换这两个元素，成为数组第一个元素aList = [func(1,2),3, ... , n];然后当前的前两个元素再传图func函数做运算，返回值返回值替换这两个元素，成为数组第一个元素aList = [func(func(1,2),3), ... , n]，直到列表只有一个元素。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "求和函数\n",
      "[1, 2, 3, 4, 5, 6]\n",
      "25\n"
     ]
    }
   ],
   "source": [
    "print \"求和函数\"\n",
    "f = lambda x,y: x + y\n",
    "print f([1,2,3],[4,5,6])\n",
    "print f(10,15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "单个参数的map, lambda调用\n",
      "[1, 4, 9, 16, 25]\n",
      "多个参数的map, lambda调用\n",
      "[5, 7, 9]\n",
      "参数为字符串\n",
      "['A', 'C', 'D', 'F']\n"
     ]
    }
   ],
   "source": [
    "print \"单个参数的map, lambda调用\"\n",
    "aList = [1,2,3,4,5]\n",
    "print map(lambda x: x**2, aList)\n",
    "\n",
    "print \"多个参数的map, lambda调用\"\n",
    "f = lambda x,y: x + y\n",
    "print map(f,[1,2,3],[4,5,6])\n",
    "\n",
    "print \"参数为字符串\"\n",
    "print map(lambda x: x.upper(), 'acdf')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "输出所有的奇数\n",
      "[1, 3, 5]\n"
     ]
    }
   ],
   "source": [
    "print \"输出所有的奇数\"\n",
    "aList = [1,2,3,4,5]\n",
    "print filter(lambda x: x%2, aList)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "列表求和\n",
      "15\n"
     ]
    }
   ],
   "source": [
    "print \"列表求和\"\n",
    "aList = [1,2,3,4,5]\n",
    "print reduce(lambda a,b: a+b, aList)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "列表取最大值\n",
      "5\n"
     ]
    }
   ],
   "source": [
    "print \"列表取最大值\"\n",
    "aList = [1,2,3,4,5]\n",
    "print reduce(lambda a,b: a if a > b else b, aList)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='ee'></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## **exec, eval (执行字符串python语句, 保留节目)**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Executing a string as a command\n"
     ]
    }
   ],
   "source": [
    "a = 'print \"Executing a string as a command\"'\n",
    "exec(a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "25"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = '(2 + 3) * 5'\n",
    "eval(a)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Reference"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* http://www.byteofpython.info/\n",
    "* http://woodpecker.org.cn/abyteofpython_cn/chinese/index.html\n",
    "* http://www.python-course.eu/\n",
    "* http://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-189-a-gentle-introduction-to-programming-using-python-january-iap-2008/"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "http://my.oschina.net/taogang/blog/286955"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Python画图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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2ik5q6f74xz/mjjvu4K677qJarZLP53nPe95TL1w+igpjsEIDaeEaBYZh9FUb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\nTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x4e7ab00>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "from mpl_toolkits.mplot3d import axes3d\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib import cm\n",
    "\n",
    "fig = plt.figure()\n",
    "ax = fig.add_subplot(111, projection='3d')\n",
    "X, Y, Z = axes3d.get_test_data(0.05)\n",
    "cset = ax.contour(X, Y, Z, cmap=cm.coolwarm)\n",
    "ax.clabel(cset, fontsize=9, inline=1)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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8fmD7djfE9Ehs0w5ANRH2+XxhJTLVUjiebCQCVaKbl5eXlnEkQsaKLnDJr0tS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\nEEJkkEy6QgiRQf8DGNnxN91ZMB8AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xa357a20>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib as mpl\n",
    "from mpl_toolkits.mplot3d import Axes3D\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "mpl.rcParams['legend.fontsize'] = 10\n",
    "\n",
    "fig = plt.figure()\n",
    "ax = fig.gca(projection='3d')\n",
    "#theta = np.linspace(-4 * np.pi, 4 * np.pi, 100)\n",
    "#z = np.linspace(-2, 2, 100)\n",
    "#r = z**2 + 1\n",
    "#x = r * np.sin(theta)\n",
    "#y = r * np.cos(theta)\n",
    "x = [1,2,3]\n",
    "y = [1.5,1,2]\n",
    "z = [2,1,3]\n",
    "ax.plot(x, y, z, label='parametric curve')\n",
    "ax.legend()\n",
    "\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.patches.Rectangle at 0xa31e198>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0xa31e828>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib import pyplot as plt\n",
    "from matplotlib.patches import Rectangle\n",
    "someX, someY = 0.5, 0.5\n",
    "fig,ax = plt.subplots()\n",
    "currentAxis = plt.gca()\n",
    "currentAxis.add_patch(Rectangle((someX - 0.1, someY - 0.1), 0.2, 0.2,\n",
    "                      alpha=1, facecolor='none'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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HjsWpp56KVCqF+++/H5s2bVID44qxZs0ajBw5EsOHD8ecOXMMX/Pyyy9j7Nix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\neFlUZVnG008/jR/+8MeYOnU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6AwC1uoETaQYA1Jyzchs76iuXlMpVSyaTqs/MDrR+0VLo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\nRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xa146208>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from mpl_toolkits.mplot3d import Axes3D\n",
    "from mpl_toolkits.mplot3d.art3d import Poly3DCollection\n",
    "import matplotlib.pyplot as plt\n",
    "fig = plt.figure()\n",
    "ax = Axes3D(fig)\n",
    "\n",
    "x = [1,2,2]\n",
    "y = [1,0,2]\n",
    "z = [1,2,0]\n",
    "verts = [zip(x, y,z)]\n",
    "ax.add_collection3d(Poly3DCollection(verts,edgecolors='red', facecolors='red'))\n",
    "x = [0,1,1]\n",
    "y = [0,0,1]\n",
    "z = [0,1,0]\n",
    "verts = [zip(x, y,z)]\n",
    "verts = [[(1,1,1), (2,0,2),(2,2,0)],[(0,0,0),(1,0,1),(1,1,0)]]\n",
    "ax.add_collection3d(Poly3DCollection(verts))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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QVdW1qRZkPlsymcxzbiFVoUGbLK0oihmtV4u9KpDc1GrtGSMGB34sNUsS8Nxz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xadbaba8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from mpl_toolkits.mplot3d import Axes3D\n",
    "from mpl_toolkits.mplot3d.art3d import Poly3DCollection\n",
    "import matplotlib.pyplot as plt\n",
    "fig = plt.figure()\n",
    "ax = Axes3D(fig)\n",
    "\n",
    "verts = [[(0.5,0.5,0.5), (1.2,0,1.2),(1.2,1.2,0)],[(0,0,0),(1,0,1),(1,1,0)]]\n",
    "ax.add_collection3d(Poly3DCollection(verts, edgecolors=['blue','red'], facecolors=['blue','red']))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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lsljlInlLoTQ/rcVLLnhHv1+1ahX279+P4uLirM7dChQs6Sq5ESKRCEKhkGz/MTlkKgGj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\nBEIf8j+LICAE2/D1yQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xaddbb00>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib import pyplot as plt\n",
    "from mpl_toolkits.mplot3d.art3d import Poly3DCollection\n",
    "\n",
    "fig = plt.figure()\n",
    "ax = fig.add_subplot(111, projection='3d')\n",
    "\n",
    "x = [0.5, 1.2, 1.2, 0, 1, 1]\n",
    "y = [0.5, 0,   1.2, 0, 0, 1]\n",
    "z = [0.5, 1.2, 0,   0, 1, 0]\n",
    "\n",
    "poly3d = [[(0.5,0.5,0.5), (1.2,0,1.2),(1.2,1.2,0)],[(0,0,0),(1,0,1),(1,1,0)]]\n",
    "ax.scatter(x,y,z)\n",
    "ax.add_collection3d(Poly3DCollection(poly3d, edgecolors=['red','blue'], facecolors='w', linewidths=1, alpha=0.5))\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.11"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}