{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# figures, subplots, axes 和 ticks 对象"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## figures, axes 和 ticks 的关系"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "这些对象的关系可以用下面的图来表示：\n",
    "\n",
    "示例图像：\n",
    "\n",
    "<img src=\"./artists_figure.png\" width = \"600\" height = \"400\" alt=\"图1\" align=left />"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "具体结构：\n",
    "\n",
    "<img src=\"./artists_tree.png\" width = \"600\" height = \"400\" alt=\"图2\" align=left />"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## figure 对象"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`figure` 对象是最外层的绘图单位，默认是以 `1` 开始编号（**MATLAB** 风格，`Figure 1, Figure 2, ...`），可以用 `plt.figure()` 产生一幅图像，除了默认参数外，可以指定的参数有：\n",
    "\n",
    "- `num` - 编号\n",
    "- `figsize` - 图像大小\n",
    "- `dpi` - 分辨率\n",
    "- `facecolor` - 背景色\n",
    "- `edgecolor` - 边界颜色\n",
    "- `frameon` - 边框\n",
    "\n",
    "这些属性也可以通过 `Figure` 对象的 `set_xxx` 方法来改变。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## subplot 和 axes 对象"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### subplot"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`subplot` 主要是使用网格排列子图："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Populating the interactive namespace from numpy and matplotlib\n"
     ]
    },
    {
     "data": {
      "image/png": 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6REQCMXSJiARi6BIRCcTQJSISiKFLRCQQQ5eISCCGLhGRQAxdIiKBGLpERAIxdImIBAoY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\nAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x3bc9860>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%pylab inline\n",
    "\n",
    "subplot(2,1,1)\n",
    "xticks([]), yticks([])\n",
    "text(0.5,0.5, 'subplot(2,1,1)',ha='center',va='center',size=24,alpha=.5)\n",
    "\n",
    "subplot(2,1,2)\n",
    "xticks([]), yticks([])\n",
    "text(0.5,0.5, 'subplot(2,1,2)',ha='center',va='center',size=24,alpha=.5)\n",
    "\n",
    "show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "更高级的可以用 `gridspec` 来绘图："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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MzEw6OztfdHfmxHw/tj//+c958803X3Q35Efquc3pNjQ0EAqFWL58Odu2bQN4\n4luznxqv18vly5eJj4+nuLj4RXdnzujYijy95zbSjbwI169fT35+PqdPn6a/v5+uri4yMjIm1L97\n9y6ff/45Y2NjvPPOOxQVFU2o09DQQHl5OVarlQ8//JD09PRx5e3t7dTW1tLe3s6DBw+w2WykpaVR\nVFREYWHhpP0cHBykuroal8uFx+PBYrGwYMECEhMTWbVqFUVFRSxYsOCpnoOzZ88yOjrK7t27iYmJ\neao2XkY6tiJP77mMdPv6+ujp6SE6OpqCggJiYmIoKCgAph4RpaSk8NZbbwFw7tw5BgcHx5V7vV7O\nnDkDQHFx8YQX5YULF/jd737HzZs3uX//PjExMfj9flwuF7///e/5/e9/P2Fetaenh6NHj/Ldd99x\n7949AKKjo/F6vbS1tXHp0iV6enqe6jlwOp04nU5WrVpFfn7+U7XxMtKx5Sc3Py/mei4j3ciLb82a\nNdjtduDhqMjhcNDY2EhpaSlRUVET9tuyZQvNzc243W7Ky8s5fPgwFouFcDjMiRMn8Pv9ZGRkTHir\nfu3aNWpqali0aBHbt2+noKAAu91OMBjE6XTyzTff0NjYSEpKyrh5x/PnzzM6OkpmZiZlZWWkpqYC\nEAwG6evro6Ghwej/bIyOjnL27Fmio6PZuXPnrPd/mc33YwsPR+X/93//x9DQEDabjWXLlrFmzRpe\ne+21p25T5o85H+mGQiHq6+sB2LBhg7F9xYoVLF68mJGREZqbm6fcf8+ePcTGxtLZ2cnVq1cBuH79\nOi6Xi5iYGPbu3YvFYjHq+3w+KisriY6O5uDBgxQVFRl/+JHR2HvvvQdAdXU1Y2Njxr6RE1s7duww\nXpSR/dLT0yktLSUzM3PWz8Hly5fxer1s3bqVxMTEWe//stKxfejevXs8ePAAu92O3++no6ODixcv\n8pvf/Ia7d+8+VZsyf8x56La2tjI0NMSiRYtYuXLluLL169cD45cbPS4+Pp6ysjIArly5gsPh4OLF\niwCUlpaSlJQ0rv6tW7cIBALk5OSQkpIyaZuZmZkkJCTg8/nGvaWMvIDv378/y99yaj09PVy/fp2k\npKSf3Nn8+X5s09LS2LVrF//wD//AP/3TP3HkyBGOHDnCrl27iI2NxePx8B//8R+MjIzM2WPKT8+c\nTy9EXnTr1q0bN2qBhy/Mqqoqbt++zfDw8JQnMQoLC3E6nTQ2NnLy5Eng4aLzjRs3Tqjb0dEBPFwr\n+sknn0z3Fr81AAAFtklEQVTZr8gLwePxGCOc3NxcHA4HFRUVvP766+Tl5ZGeno7V+nT/i8LhsLEm\n9y//8i8nfZv9Yzafjy3AG2+8MWFbbGwsGzduJCMjgy+++IKhoSFqamqMOWyRx81p6Pp8PpqamoA/\njXwelZSURFZWFh0dHTQ0NEz6RxxRVlZGU1MTwWAQu93Ou+++O2m9oaEhAAKBAIFA4Il9DAaDxvcl\nJSUMDAzQ0dFBdXU11dXVREVFkZWVRUFBARs2bCA6euZPUW1tLd3d3eTn57Nq1aoZ7/djMN+P7ZOk\npqZSWFhIXV0dzc3NCl2Z0pyG7s2bN415taNHj05b1+FwTPvCbGxsNF5Efr+f3t7eCW9p4U9nkjdv\n3kxpaems+hsXF8fhw4dpbW2lubmZ9vZ2ent7cbvduN1uampq+OCDD4iPj39iW4/OP/75n/85o6Oj\n48ofDY3R0VFGR0eJior60YyG5/OxnamMjAzq6uomrM4QedSchu5083mP6+3tpa+vj+Tk5AllAwMD\nnDt3DoDk5GT6+vo4efIkf/d3fzfhMsuFCxcC8P333z91v3NycsjJyQEehufNmze5dOkSg4ODnDt3\njr/6q796Yhs+n88I2n/7t3+btu6vf/1r4OGIcffu3U/dbzPN52MrMpfmLHQHBgaMM8Yff/wxCQkJ\nk9YLh8NUVFTQ3NyMw+GgpKRkXHkoFKK8vJxgMEhOTg779+/ns88+o7+/nzNnzrBv375x9bOysqiv\nr6etrY1gMPjMbxkjc3QAp0+fpq2t7Znam87j86IvKx3bmYk8Rz+lFSsy9+YsdCPrN1NTU6c80xxR\nUFBAc3MzDQ0N/MVf/MW48KmqqqK7u5u4uDjjSq69e/fyxRdfcPPmTVavXs3PfvazcW2dP3+ekZER\nrly5Mu1c2sjIiDGaCofDhMPhKU+sRF7gj84TTichIYF//ud/nrLc7XZz7Ngx4Md3l7H5fmxnore3\nl8bGRuDhSTyRqczJkrFwOGys35zJ1VerV6/GarUyNDTE7du3je1dXV1UVVUBsHPnTuOuVWlpacYN\nRs6ePYvX6zX2iYuL4+233wbg22+/5dSpUwwMDBjlgUAAl8vFqVOn+PLLL43tfr+fTz/9lKtXr9LX\n10coFDJ+l9bWVi5dugTwkzshNls6tg/V1dXx3//937S0tODz+YztPp+P7777jmPHjhEKhVi4cCE/\n//nPZ9yuzD9zMtJ1u914PB5gZi/M2NhYsrOzuXPnDnV1deTm5hIIBCgvLyccDlNYWDjhevpt27bR\n0tJCZ2cnJ06c4P333zfKNm3ahM/n4/Lly9y4cYMbN24QExNDVFTUuBfI42+LPR4PlZWVVFZWYrVa\nsdls+P1+4wROYmLirE/g/NTo2D4UDoe5desWt27dAh7e3tFqtY7rw+LFi9m/f7/u5yDTmpPQjbz9\nXLJkCcuWLZvRPmvXruXOnTs4nU58Ph8XL17k3r174xbQP8pisbBnzx6OHj2Ky+Xi2rVrbN682Sgv\nLi4mLy+P2tpa3G43Xq+XQCBAfHw8ycnJZGdns27dOqO+3W7nwIEDtLa20tHRgdfrZXh4GJvNxtKl\nS8nLy2PTpk1T3jd1vtCxfSg7O5vt27fT0dHBwMAAw8PDjI6OsnDhQpKTk1mzZg1/9md/Nu//XuTJ\nLNPdvMNisYR1c4+Xww/3KZiTM286ri+PuTyuP7SnY/sSmO646jPSRERMpNAVETGRQldExEQKXRER\nEyl0RURMpNAVETGRQldExEQKXREREyl0RURMpNAVETGRQldExEQKXREREyl0RURMpNAVETGRQldE\nxEQKXREREyl0RURMpNAVETGRQldExEQKXREREyl0RURMpNAVETGRQldExEQKXRERE0U/qYLFYjGj\nH2IyHdefLh3bl5slHA6/6D6IiMwbml4QETGRQldExEQKXREREyl0RURMpNAVETHR/wOTsu2dHjKM\ntAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xa0753c8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.gridspec as gridspec\n",
    "\n",
    "G = gridspec.GridSpec(3, 3)\n",
    "\n",
    "axes_1 = subplot(G[0, :])\n",
    "xticks([]), yticks([])\n",
    "text(0.5,0.5, 'Axes 1',ha='center',va='center',size=24,alpha=.5)\n",
    "\n",
    "axes_2 = subplot(G[1,:-1])\n",
    "xticks([]), yticks([])\n",
    "text(0.5,0.5, 'Axes 2',ha='center',va='center',size=24,alpha=.5)\n",
    "\n",
    "axes_3 = subplot(G[1:, -1])\n",
    "xticks([]), yticks([])\n",
    "text(0.5,0.5, 'Axes 3',ha='center',va='center',size=24,alpha=.5)\n",
    "\n",
    "axes_4 = subplot(G[-1,0])\n",
    "xticks([]), yticks([])\n",
    "text(0.5,0.5, 'Axes 4',ha='center',va='center',size=24,alpha=.5)\n",
    "\n",
    "axes_5 = subplot(G[-1,-2])\n",
    "xticks([]), yticks([])\n",
    "text(0.5,0.5, 'Axes 5',ha='center',va='center',size=24,alpha=.5)\n",
    "\n",
    "show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## axes 对象"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`subplot` 返回的是 `Axes` 对象，但是 `Axes` 对象相对于 `subplot` 返回的对象来说要更自由一点。`Axes` 对象可以放置在图像中的任意位置："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0xa00b0b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "axes([0.1,0.1,.8,.8])\n",
    "xticks([]), yticks([])\n",
    "text(0.6,0.6, 'axes([0.1,0.1,.8,.8])',ha='center',va='center',size=20,alpha=.5)\n",
    "\n",
    "axes([0.2,0.2,.3,.3])\n",
    "xticks([]), yticks([])\n",
    "text(0.5,0.5, 'axes([0.2,0.2,.3,.3])',ha='center',va='center',size=16,alpha=.5)\n",
    "\n",
    "show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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+b+3t3r0bPz8/p2b9TaQTJ05QX1/Prl27nmq9vb29fPbZZ2zcuJGEhIQJKfPQoUP09PSQ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\nwlkIIVRIwlkIIVRIwlkIIVTo/wC1F2xbdiTycgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xa6ff128>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "axes([0.1,0.1,.5,.5])\n",
    "xticks([]), yticks([])\n",
    "text(0.1,0.1, 'axes([0.1,0.1,.8,.8])',ha='left',va='center',size=16,alpha=.5)\n",
    "\n",
    "axes([0.2,0.2,.5,.5])\n",
    "xticks([]), yticks([])\n",
    "text(0.1,0.1, 'axes([0.2,0.2,.5,.5])',ha='left',va='center',size=16,alpha=.5)\n",
    "\n",
    "axes([0.3,0.3,.5,.5])\n",
    "xticks([]), yticks([])\n",
    "text(0.1,0.1, 'axes([0.3,0.3,.5,.5])',ha='left',va='center',size=16,alpha=.5)\n",
    "\n",
    "axes([0.4,0.4,.5,.5])\n",
    "xticks([]), yticks([])\n",
    "text(0.1,0.1, 'axes([0.4,0.4,.5,.5])',ha='left',va='center',size=16,alpha=.5)\n",
    "\n",
    "show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "后面的 `Axes` 对象会覆盖前面的内容。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## ticks 对象"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "ticks 用来注释轴的内容，我们可以通过控制它的属性来决定在哪里显示轴、轴的内容是什么等等。"
   ]
  }
 ],
 "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
}