06-matplotlib/06.04-working-with-text---math-expression.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 处理文本（数学表达式）"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "在字符串中使用一对 `$$` 符号可以利用 `Tex` 语法打出数学表达式，而且并不需要预先安装 `Tex`。在使用时我们通常加上 `r` 标记表示它是一个原始字符串（raw string）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXcAAAEKCAYAAADpfBXhAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xa0d7518>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plain text\n",
    "plt.title('alpha > beta')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXcAAAEKCAYAAADpfBXhAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xa162860>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# math text\n",
    "plt.title(r'$\\alpha > \\beta$')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 上下标"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "使用 `_` 和 `^` 表示上下标：\n",
    "\n",
    "$\\alpha_i > \\beta_i$：\n",
    "\n",
    "    r'$\\alpha_i > \\beta_i$'\n",
    "\n",
    "$\\sum\\limits_{i=0}^\\infty x_i$：\n",
    "\n",
    "    r'$\\sum_{i=0}^\\infty x_i$'\n",
    "\n",
    "注：\n",
    "\n",
    "- 希腊字母和特殊符号可以用 '\\ + 对应的名字' 来显示\n",
    "- `{}` 中的内容属于一个部分；要打出花括号是需要使用 `\\{\\}`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 分数，二项式系数，stacked numbers"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$\\frac{3}{4}, \\binom{3}{4}, \\stackrel{3}{4}$：\n",
    "\n",
    "    r'$\\frac{3}{4}, \\binom{3}{4}, \\stackrel{3}{4}$'\n",
    "\n",
    "$\\frac{5 - \\frac{1}{x}}{4}$：\n",
    "\n",
    "    r'$\\frac{5 - \\frac{1}{x}}{4}$'\n",
    "\n",
    "在 Tex 语言中，括号始终是默认的大小，如果要使括号大小与括号内部的大小对应，可以使用 `\\left` 和 `\\right` 选项：\n",
    "\n",
    "$(\\frac{5 - \\frac{1}{x}}{4})$\n",
    "\n",
    "    r'$(\\frac{5 - \\frac{1}{x}}{4})$'\n",
    "\n",
    "$\\left(\\frac{5 - \\frac{1}{x}}{4}\\right)$：\n",
    "\n",
    "    r'$\\left(\\frac{5 - \\frac{1}{x}}{4}\\right)$'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 根号"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$\\sqrt{2}$：\n",
    "\n",
    "    r'$\\sqrt{2}$'\n",
    "\n",
    "$\\sqrt[3]{x}$：\n",
    "\n",
    "    r'$\\sqrt[3]{x}$'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 特殊字体"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "默认显示的字体是斜体，不过可以使用以下方法显示不同的字体：\n",
    "\n",
    "命令|显示\n",
    "--|--\n",
    "\\mathrm{Roman}|$\\mathrm{Roman}$\n",
    "\\mathit{Italic}|$\\mathit{Italic}$\n",
    "\\mathtt{Typewriter}|$\\mathtt{Typewriter}$\n",
    "\\mathcal{CALLIGRAPHY}|$\\mathcal{CALLIGRAPHY}$\n",
    "\\mathbb{blackboard}|$\\mathbb{blackboard}$\n",
    "\\mathfrak{Fraktur}|$\\mathfrak{Fraktur}$\n",
    "\\mathsf{sansserif}|$\\mathsf{sansserif}$\n",
    "\n",
    "$s(t) = \\mathcal{A}\\ \\sin(2 \\omega t)$：\n",
    "\n",
    "    s(t) = \\mathcal{A}\\ \\sin(2 \\omega t)\n",
    "\n",
    "注：\n",
    "\n",
    "- Tex 语法默认忽略空格，要打出空格使用 `'\\ '`\n",
    "- \\sin 默认显示为 Roman 字体"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 音调"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "命令|结果\n",
    "--|--\n",
    "`\\acute a`| $\\acute a$\n",
    "`\\bar a`| $\\bar a$\n",
    "`\\breve a` | $\\breve a$\n",
    "`\\ddot a`| $\\ddot a$\n",
    "`\\dot a` | $\\dot a$\n",
    "`\\grave a`| $\\grave a$\n",
    "`\\hat a`| $\\hat a$\n",
    "`\\tilde a` | $\\tilde a$\n",
    "`\\vec a` | $\\vec a$\n",
    "`\\overline{abc}`|$\\overline{abc}$\n",
    "`\\widehat{xyz}`|$\\widehat{xyz}$\n",
    "`\\widetilde{xyz}`|$\\widetilde{xyz}$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 特殊字符表"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "参见：http://matplotlib.org/users/mathtext.html#symbols"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 例子"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAY0AAAEfCAYAAAC9CZqZAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\noFnSMMYYE7T/Bxi7FV50SWh9AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xa567160>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "t = np.arange(0.0, 2.0, 0.01)\n",
    "s = np.sin(2*np.pi*t)\n",
    "\n",
    "plt.plot(t,s)\n",
    "plt.title(r'$\\alpha_i > \\beta_i$', fontsize=20)\n",
    "plt.text(1, -0.6, r'$\\sum_{i=0}^\\infty x_i$', fontsize=20)\n",
    "plt.text(0.6, 0.6, r'$\\mathcal{A}\\ \\mathrm{sin}(2 \\omega t)$',\n",
    "         fontsize=20)\n",
    "plt.xlabel('time (s)')\n",
    "plt.ylabel('volts (mV)')\n",
    "plt.show()"
   ]
  }
 ],
 "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": "2.7.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}