{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1.3 Python的安装与使用"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "在众多的编程语言中，Python比较适合于遥感模式识别的实验课程。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1.3.1 选择Python的理由"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### (1) 学习成本低，受欢迎"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Python使用的人越来越多，越来越受人欢迎。使用的人越多，已有的代码工具就越多，学习成本越低。可以在Google Trends 上面查看Python和其他语言的热度图，其中的热度代表该词汇在该地区搜索词汇中所占比率："
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![pythonmap](images/PythonHeatMap.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### (2) Arcgis 的脚本语言"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Arcgis以Python为脚本语言，可以进行批量处理，使得制图和处理的流程全自动化."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![arcgis](images/Arcgis.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1.3.2 Python的安装"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Python的安装推荐使用Anaconda工具包，自带了常用的一些代码库。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Python语言简单灵活，开发效率高，有众多的机器学习算法的代码库支持。从Arcgis 10.0版本开始，Arcgis支持Python脚本调用。Ipython是Python的一个工具包，可以进行交互式的操作与输出。\n",
    "\n",
    "本节将介绍关于 Ipython Notebook 的四个内容：\n",
    "1. 功能用途\n",
    "2. 软件安装\n",
    "3. 使用方法\n",
    "4. LaTeX与Markdown"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### (1) IPython Notebook的功能与用途"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ipython Notebook应用程序，是2011年由美国加州大学伯克利分校数据科学家Fernando Pérez和加州州立综合科技大学计算物理学家Brian Granger带领的研究团队共同开发的。它是一个基于python语言的**交互式**命令界面。最大的亮点在于，以笔机的形式记录显示：\n",
    "* 文章思路\n",
    "* 编码过程\n",
    "* 代码注释\n",
    "* 输出结果\n",
    "* 分享演示"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "不同于普通的注释，它的注释可以包含以下内容：\n",
    "1. **公式**：LaTeX数学公式\n",
    "2. **文本**：Markdown文本\n",
    "3. **图表**\n",
    "4. **视频**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![Imageofjupyter](images/jupyterpreview.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "IPython 4.0正式发布后，IPython分离成IPython和Jupyter，支持Ruby、R语言等，所以在左上角可以看到Jupyter的标识。这样的一款软件是易于使用的，按照下面的步骤就可以轻松获取它。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### (2) 软件安装"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "有多种安装Ipython Notebook的方式，推荐使用Anaconda安装包。Anaconda集成了大量的常用数据分析、科学计算的库，可以在Windows、Mac和Linux下一键安装。Python有很多的版本，Anaconda都支持。这里我们使用Python 3.6 版本的Anaconda安装包，这个版本的python代码库是功能最全面的。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "软件可以一键安装，安装包下载地址为：\n",
    "https://www.continuum.io/downloads"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### (3) 运行启动"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. 在windows系统下，选择自己的目录；\n",
    "2. 在空白处，点击“shift+右键”，弹出菜单；\n",
    "3. 在菜单中选择“打开命令行”选项；\n",
    "4. 在命令行中输入：   \n",
    "```bash\n",
    "jupyter notebook\n",
    "```\n",
    "将会自动在浏览器中弹出软件页面。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![jupyter](images/new.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### (4) 使用方法"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "这里简单介绍最常用到的操作。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**%pylab** 可以导入常用的函数名称空间，直接使用常用函数。 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using matplotlib backend: MacOSX\n",
      "Populating the interactive namespace from numpy and matplotlib\n"
     ]
    }
   ],
   "source": [
    "%pylab"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**inline** 是把显示的图片嵌入到notebook中，而不是单独显示一个窗口。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Populating the interactive namespace from numpy and matplotlib\n"
     ]
    }
   ],
   "source": [
    "%pylab inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Shift+Tab** 快捷键用来快速查看函数信息。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<function numpy.core.function_base.linspace>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "linspace"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "函数名后面跟随一个问号，可以查看函数的帮助信息。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "sum?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "类名后面跟随两个问号，可以显示定义类的源码"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "sum??"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**%run** 命令把文件中的代码执行一遍，并不复制。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "%run PythonCode/lorenz.py"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**%load** 命令会把文件中的代码复制到notebook里。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "```bash\n",
    "%load PythonCode/lorenz.py\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### (5) Python 语言快速概览 "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "课程中将简单地介绍Python语言的语法，不涉及具体编程应用的内容。对于完全不熟悉Python语言的同学，还需要适当额外补充相应的知识和练习。涉及到的Python编程为以下主题：\n",
    "* 数据基本类型，表达式，变量及其赋值\n",
    "* 基本数据结构\n",
    "* 控制语句\n",
    "* 函数和对象"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "推荐书目为："
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Barry P. Head First Python: A Brain-Friendly Guide[M]. \" O'Reilly Media, Inc.\", 2016."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Hetland M L. Python 基础教程[J]. 2014."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}