1.4Matplotlib：绘图.ipynb

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   "cells": [
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     "cell_type": "markdown",
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     "source": [
      "## 1.4.1 \u7b80\u4ecb\n",
      "[Matplotlib] (http://matplotlib.org/) \u53ef\u80fd\u662fPython\u60df\u4e00\u4e00\u4e2a\u6700\u5e7f\u6cdb\u4f7f\u7528\u7684\u4e8c\u7ef4\u56fe\u5305\u3002\u5b83\u540c\u65f6\u63d0\u4f9b\u4e86\u4ecePython\u4e2d\u53ef\u89c6\u5316\u6570\u636e\u975e\u5e38\u7684\u5feb\u901f\u65b9\u5f0f\u4ee5\u53ca\u591a\u79cd\u683c\u5f0f\u7684\u51fa\u7248\u8d28\u91cf\u56fe\u7247\u3002\u6211\u4eec\u5c06\u5728\u4ea4\u4e92\u6a21\u5f0f\u4e0b\u7814\u7a76Matplotlib\uff0c\u5305\u542b\u5927\u591a\u6570\u7684\u5e38\u7528\u6848\u4f8b\u3002\n",
      "\n",
      "### 1.4.1.1 IPython\u548cpylab\u6a21\u5f0f\n",
      "\n",
      "[IPython](http://ipython.org/)\u662f\u5f3a\u5316\u7248\u4ea4\u4e92Python shell\uff0c\u6709\u8bb8\u591a\u6709\u8da3\u7684\u529f\u80fd\uff0c\u5305\u62ec\uff1a\u8f93\u5165\u8f93\u51fa\u7684\u547d\u540d\u3001\u8bbf\u95eeshell\u547d\u4ee4\u6539\u8fdb\u9519\u8bef\u6392\u9664\u7b49\u3002\u5b83\u4f4d\u4e8ePython\u4e2d\u7684\u79d1\u5b66\u8ba1\u7b97\u5de5\u4f5c\u6d41\u7684\u6838\u5fc3\uff0c\u8981\u8ba9\u5b83\u4e0eMatplotlib\u7684\u7ed3\u5408\u4f7f\u7528\uff1a\n",
      "\n",
      "\u7528\u547d\u4ee4\u884c\u53c2\u6570 `-pylab`\uff08`--pylab` \u4eceIPython0.12\u5f00\u59cb\uff09\u542f\u52a8IPython\uff0c\u83b7\u5f97\u5e26\u6709Matlab/Mathematica\u7c7b\u4f3c\u529f\u80fd\u7684\u4ea4\u4e92Matplotlib session\u3002\n",
      "\n",
      "### 1.4.1.2 pylab\n",
      "\n",
      "*pylab*\u63d0\u4f9b\u4e86matplotlib\u9762\u5411\u5bf9\u8c61\u7684\u7ed8\u56fe\u5e93\u7684\u7a0b\u5e8f\u63a5\u53e3\u3002\u5b83\u7684\u6a21\u578b\u4e0eMatlab\u2122\u975e\u5e38\u76f8\u8fd1\u3002\u56e0\u6b64\uff0cpylab\u4e2d\u7684\u7edd\u5927\u591a\u6570\u7ed8\u56fe\u547d\u4ee4Matlab\u2122\u90fd\u6709\u5e26\u6709\u76f8\u4f3c\u51fd\u6570\u7684\u7c7b\u4f3c\u5b9e\u73b0\u3002\u91cd\u8981\u7684\u547d\u4ee4\u4f1a\u4ee5\u4ea4\u4e92\u4f8b\u5b50\u6765\u89e3\u91ca\u3002\n",
      "\n",
      "## 1.4.2 \u7b80\u5355\u7ed8\u56fe\n",
      "\n",
      "\u5728\u8fd9\u4e2a\u90e8\u5206\uff0c\u6211\u4eec\u5c06\u5728\u540c\u4e00\u4e2a\u56fe\u50cf\u4e2d\u7ed8\u5236cosine\u548csine\u51fd\u6570\u3002\u4ece\u9ed8\u8ba4\u8bbe\u7f6e\u5f00\u59cb\uff0c\u6211\u4eec\u5c06\u4e0d\u65ad\u4e30\u5bcc\u56fe\u7247\uff0c\u8ba9\u5b83\u770b\u8d77\u6765\u66f4\u6f02\u4eae\u3002\n",
      "\n",
      "\u7b2c\u4e00\u6b65\u83b7\u5f97sine\u548ccosine\u51fd\u6570\u7684\u6570\u636e\uff1a"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import numpy as np\n",
      "\n",
      "X = np.linspace(-np.pi, np.pi, 256, endpoint=True)\n",
      "C, S = np.cos(X), np.sin(X)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 2
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "`X`\u73b0\u5728\u662fNumpy\u6570\u7ec4\uff0c\u8303\u56f4\u662f`-\u03c0`\u5230`+\u03c0`\u4e4b\u95f4\uff08\u5305\u542b\uff09\u7684256\u4e2a\u503c\u3002C\u662fcosine\uff08256\u4e2a\u503c\uff09\uff0c\u800cS\u662fsine\uff08256\u4e2a\u503c\uff09\n",
      "\n",
      "\u8981\u8fd0\u884c\u4f8b\u5b50\uff0c\u4f60\u53ef\u4ee5\u5728IPython\u7684\u4ea4\u4e92session\u4e2d\u8f93\u5165\u8fd9\u4e9b\u547d\u4ee4\uff1a\n",
      "\n",
      "```\n",
      "ipython --pylab\n",
      "```\n",
      "\n",
      "\u8fd9\u4f1a\u5c06\u6211\u4eec\u5e26\u5230IPython\u63d0\u793a\u7b26\uff1a\n",
      "\n",
      "```\n",
      "IPython 2.3.1 -- An enhanced Interactive Python.\n",
      "?         -> Introduction and overview of IPython's features.\n",
      "%quickref -> Quick reference.\n",
      "help      -> Python's own help system.\n",
      "object?   -> Details about 'object', use 'object??' for extra details.\n",
      "Using matplotlib backend: MacOSX\n",
      "```\n",
      "\n",
      "\u4f60\u53ef\u4ee5\u4e0b\u8f7d\u6bcf\u4e2a\u793a\u4f8b\uff0c\u7136\u540e\u7528\u5e73\u5e38\u7684Python\u8fd0\u884c\uff0c\u4f46\u662f\uff0c\u4f60\u5c06\u6ca1\u6cd5\u52a8\u6001\u7684\u6570\u636e\u64cd\u4f5c\uff1a\n",
      "\n",
      "```\n",
      "python exercice_1.py\n",
      "```\n",
      "\n",
      "\u901a\u8fc7\u70b9\u51fb\u5bf9\u5e94\u7684\u56fe\u7247\uff0c\u4f60\u53ef\u4ee5\u83b7\u5f97\u6bcf\u4e00\u6b65\u7684\u6e90\u7801\u3002\n",
      "\n",
      "### 1.4.2.1 \u7528\u9ed8\u8ba4\u8bbe\u7f6e\u7ed8\u56fe\n",
      "\n",
      "---\n",
      "**\u63d0\u793a**\uff1a\u6587\u6863\n",
      "\n",
      "* [plot\u6559\u7a0b](http://matplotlib.sourceforge.net/users/pyplot_tutorial.html)\n",
      "* [plot()\u547d\u4ee4](http://matplotlib.sourceforge.net/api/pyplot_api.html#matplotlib.pyplot.plot)\n",
      "\n",
      "---\n",
      "\n",
      "Matplotlib\u6709\u4e00\u7ec4\u9ed8\u8ba4\u8bbe\u7f6e\uff0c\u5141\u8bb8\u81ea\u5b9a\u4e49\u6240\u6709\u7684\u5c5e\u6027\u3002\u4f60\u51e0\u4e4e\u53ef\u4ee5\u63a7\u5236\u5728matplotlib\u4e2d\u7684\u6240\u6709\u5c5e\u6027\uff1a\u56fe\u7247\u5927\u5c0f\u548cdpi\u3001\u7ebf\u957f\u5ea6\u3001\u989c\u8272\u548c\u6837\u5f0f\u3001\u5750\u6807\u8f74\u3001\u5750\u6807\u8f74\u548c\u7f51\u683c\u5c5e\u6027\u3001\u6587\u672c\u548c\u5b57\u4f53\u5c5e\u6027\u7b49\u7b49\u3002\n",
      "\n",
      "```python\n",
      "import pylab as pl\n",
      "import numpy as np\n",
      "\n",
      "X = np.linspace(-np.pi, np.pi, 256, endpoint=True)\n",
      "C, S = np.cos(X), np.sin(X)\n",
      "\n",
      "pl.plot(X, C)\n",
      "pl.plot(X, S)\n",
      "\n",
      "pl.show()\n",
      "```\n",
      "\n",
      "<a class=\"reference external image-reference\" href=\"http://scipy-lectures.github.io/intro/matplotlib/auto_examples/plot_exercice_1.html\"><img alt=\"plot_exercice_1_1.png\" class=\"align-right\" src=\"http://scipy-lectures.github.io/_images/plot_exercice_1_1.png\" style=\"width: 280.0px; height: 210.0px;\"></a>"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "### 1.4.2.2 \u9ed8\u8ba4\u503c\u793a\u4f8b\n",
      "\n",
      "---\n",
      "**\u63d0\u793a**\uff1a\u6587\u6863\n",
      "\n",
      "* [\u81ea\u5b9a\u4e49matplotlib](http://matplotlib.sourceforge.net/users/customizing.html)\n",
      "\n",
      "---\n",
      "\n",
      "\u5728\u4e0b\u9762\u7684\u811a\u672c\u4e2d\uff0c\u6211\u4eec\u6807\u793a\uff08\u5907\u6ce8\uff09\u4e86\u5f71\u54cd\u7ed8\u56fe\u5916\u89c2\u7684\u6240\u6709\u56fe\u7247\u8bbe\u7f6e\u3002\n",
      "\n",
      "\u8fd9\u4e9b\u8bbe\u7f6e\u88ab\u663e\u5f0f\u7684\u8bbe\u7f6e\u4e3a\u9ed8\u8ba4\u503c\uff0c\u4f46\u662f\u73b0\u5728\u4f60\u53ef\u4ee5\u4ea4\u4e92\u7684\u5b9e\u9a8c\u8fd9\u4e9b\u503c\u4ee5\u4fbf\u9a8c\u8bc1\u4ed6\u4eec\u7684\u6548\u679c\uff08\u770b\u4e00\u4e0b\u4e0b\u9762\u7684[\u7ebf\u5c5e\u6027](http://scipy-lectures.github.io/intro/matplotlib/matplotlib.html#line-properties)\u548c[\u7ebf\u6837\u5f0f](http://scipy-lectures.github.io/intro/matplotlib/matplotlib.html#line-styles))\u3002\n",
      "\n",
      "```python\n",
      "import pylab as pl\n",
      "import numpy as np\n",
      "\n",
      "# \u521b\u5efa\u4e00\u4e2a\u5927\u5c0f\u4e3a 8X6 \u82f1\u5bf8\uff0c\u6bcf\u82f1\u5bf880\u4e2a\u70b9\u7684\u56fe\u7247\n",
      "pl.figure(figsize=(8, 6), dpi=80)\n",
      "\n",
      "# \u4ece1X1\u7684\u7f51\u683c\u521b\u5efa\u4e00\u4e2a\u5b50\u56fe\u7247\n",
      "pl.subplot(1, 1, 1)\n",
      "\n",
      "X = np.linspace(-np.pi, np.pi, 256, endpoint=True)\n",
      "C, S = np.cos(X), np.sin(X)\n",
      "\n",
      "# \u7528\u5bbd\u5ea6\u4e3a1\uff08\u50cf\u7d20\uff09\u7684\u84dd\u8272\u8fde\u7eed\u76f4\u7ebf\u7ed8\u5236cosine\n",
      "pl.plot(X, C, color=\"blue\", linewidth=1.0, linestyle=\"-\")\n",
      "\n",
      "# \u7528\u5bbd\u5ea6\u4e3a1\uff08\u50cf\u7d20\uff09\u7684\u7eff\u8272\u8fde\u7eed\u76f4\u7ebf\u7ed8\u5236sine\n",
      "pl.plot(X, S, color=\"green\", linewidth=1.0, linestyle=\"-\")\n",
      "\n",
      "# \u8bbe\u7f6ex\u8f74\u7684\u6781\u503c\n",
      "pl.xlim(-4.0, 4.0)\n",
      "\n",
      "# \u8bbe\u7f6ex\u8f74\u7684\u523b\u5ea6\u503c\n",
      "pl.xticks(np.linspace(-4, 4, 9, endpoint=True))\n",
      "\n",
      "# \u8bbe\u7f6ey\u8f74\u7684\u6781\u503c\n",
      "pl.ylim(-1.0, 1.0)\n",
      "\n",
      "# \u8bbe\u7f6ey\u8f74\u7684\u523b\u5ea6\u503c\n",
      "pl.yticks(np.linspace(-1, 1, 5, endpoint=True))\n",
      "\n",
      "# \u752872dpi\u4fdd\u5b58\u56fe\u7247\n",
      "# savefig(\"exercice_2.png\", dpi=72)\n",
      "\n",
      "# \u5728\u5c4f\u5e55\u4e0a\u663e\u793a\u7ed3\u679c\n",
      "pl.show()\n",
      "```\n",
      "\n",
      "<a class=\"reference external image-reference\" href=\"http://scipy-lectures.github.io/intro/matplotlib/auto_examples/plot_exercice_2.html\"><img alt=\"plot_exercice_2_1.png\" class=\"align-right\" src=\"http://scipy-lectures.github.io/_images/plot_exercice_2_1.png\" style=\"width: 280.0px; height: 210.0px;\"></a>\n",
      "\n",
      "\n",
      "### 1.4.2.3 \u6539\u53d8\u989c\u8272\u548c\u7ebf\u5bbd\u5ea6\n",
      "\n",
      "---\n",
      "**\u63d0\u793a**\uff1a\u6587\u6863\n",
      "\n",
      "* [\u63a7\u5236\u7ebf\u5c5e\u6027](http://matplotlib.sourceforge.net/users/pyplot_tutorial.html#controlling-line-properties)\n",
      "* [\u7ebfAPI](http://matplotlib.sourceforge.net/api/artist_api.html#matplotlib.lines.Line2D)\n",
      "\n",
      "---\n",
      "\n",
      "\u9996\u5148\uff0c\u6211\u4eec\u60f3\u8981cosine\u662f\u84dd\u8272\uff0csine\u662f\u7ea2\u8272\uff0c\u4e24\u8005\u90fd\u662f\u7a0d\u7a0d\u7c97\u4e00\u70b9\u7684\u7ebf\u3002\u6211\u4eec\u4e5f\u6539\u53d8\u4e86\u4e00\u70b9\u56fe\u7247\u7684\u5927\u5c0f\uff0c\u8ba9\u5b83\u66f4\u52a0\u6c34\u5e73\u3002\n",
      "\n",
      "```python\n",
      "pl.figure(figsize=(10, 6), dpi=80)\n",
      "pl.plot(X, C, color=\"blue\", linewidth=2.5, linestyle=\"-\")\n",
      "pl.plot(X, S, color=\"red\",  linewidth=2.5, linestyle=\"-\")\n",
      "```\n",
      "\n",
      "<a class=\"reference external image-reference\" href=\"http://scipy-lectures.github.io/intro/matplotlib/auto_examples/plot_exercice_3.html\"><img alt=\"plot_exercice_3_1.png\" class=\"align-right\" src=\"http://scipy-lectures.github.io/_images/plot_exercice_3_1.png\" style=\"width: 280.0px; height: 175.0px;\"></a>\n",
      "\n",
      "### 1.4.2.4 \u8bbe\u7f6e\u6781\u503c\n",
      "\n",
      "---\n",
      "**\u63d0\u793a**\uff1a\u6587\u6863\n",
      "[xlim()\u547d\u4ee4](http://matplotlib.sourceforge.net/api/pyplot_api.html#matplotlib.pyplot.xlim)\n",
      "[ylim()\u547d\u4ee4](http://matplotlib.sourceforge.net/api/pyplot_api.html#matplotlib.pyplot.ylim)\n",
      "\n",
      "---\n",
      "\n",
      "\u5f53\u524d\u7684\u56fe\u7247\u7684\u6781\u503c\u9650\u5236\u592a\u62e5\u6324\u4e86\uff0c\u6211\u4eec\u5e0c\u671b\u7559\u4e00\u70b9\u7a7a\u95f4\u4ee5\u4fbf\u6e05\u6670\u7684\u770b\u5230\u6240\u6709\u7684\u6570\u636e\u70b9\u3002\n",
      "\n",
      "```python\n",
      "pl.xlim(X.min() * 1.1, X.max() * 1.1)\n",
      "pl.ylim(C.min() * 1.1, C.max() * 1.1)\n",
      "```\n",
      "\n",
      "<a class=\"reference external image-reference\" href=\"http://scipy-lectures.github.io/intro/matplotlib/auto_examples/plot_exercice_4.html\"><img alt=\"plot_exercice_4_1.png\" class=\"align-right\" src=\"http://scipy-lectures.github.io/_images/plot_exercice_4_1.png\" style=\"width: 280.0px; height: 175.0px;\"></a>\n",
      "\n",
      "### 1.4.2.5 \u8bbe\u7f6e\u5750\u6807\u8f74\u523b\u5ea6\u503c\n",
      "\n",
      "**\u63d0\u793a**\uff1a\u6587\u6863\n",
      "* [xticks()\u547d\u4ee4](http://matplotlib.sourceforge.net/api/pyplot_api.html#matplotlib.pyplot.xticks)\n",
      "* [yticks()\u547d\u4ee4](http://matplotlib.sourceforge.net/api/pyplot_api.html#matplotlib.pyplot.yticks)\n",
      "* [\u523b\u5ea6\u5bb9\u5668](http://matplotlib.sourceforge.net/users/artists.html#axis-container)\n",
      "* [\u523b\u5ea6\u4f4d\u7f6e\u548c\u683c\u5f0f](http://matplotlib.sourceforge.net/api/ticker_api.html)\n",
      "\n",
      "\u73b0\u5728\u7684\u523b\u5ea6\u4e0d\u592a\u7406\u60f3\uff0c\u56e0\u4e3a\u4ed6\u4eec\u6ca1\u6709\u663e\u793a\u5bf9\u4e8esine\u548ccosine\u6709\u610f\u4e49\u7684\u503c\uff08+/-\u03c0,+/-\u03c0/2\uff09\u3002\u6211\u4eec\u5c06\u6539\u53d8\u8fd9\u4e9b\u523b\u5ea6\uff0c\u8ba9\u4ed6\u4eec\u53ea\u663e\u793a\u8fd9\u4e9b\u503c\u3002\n",
      "\n",
      "```python\n",
      "pl.xticks([-np.pi, -np.pi/2, 0, np.pi/2, np.pi])\n",
      "pl.yticks([-1, 0, +1])\n",
      "```\n",
      "\n",
      "<a class=\"reference external image-reference\" href=\"http://scipy-lectures.github.io/intro/matplotlib/auto_examples/plot_exercice_5.html\"><img alt=\"plot_exercice_5_1.png\" class=\"align-right\" src=\"http://scipy-lectures.github.io/_images/plot_exercice_5_1.png\" style=\"width: 280.0px; height: 175.0px;\"></a>\n",
      "\n",
      "### 1.4.2.6 \u8bbe\u7f6e\u523b\u5ea6\u6807\u7b7e\n",
      "\n",
      "**\u63d0\u793a**\uff1a\u6587\u6863\n",
      "* [\u64cd\u4f5c\u6587\u672c](http://matplotlib.sourceforge.net/users/index_text.html)\n",
      "* [xticks()\u547d\u4ee4](http://matplotlib.sourceforge.net/api/pyplot_api.html#matplotlib.pyplot.xticks)\n",
      "* [yticks()\u547d\u4ee4](http://matplotlib.sourceforge.net/api/pyplot_api.html#matplotlib.pyplot.yticks)\n",
      "* [set_xticklabels()](http://matplotlib.sourceforge.net/api/axes_api.html?#matplotlib.axes.Axes.set_xticklabels)\n",
      "* [set_yticklabels()](http://matplotlib.sourceforge.net/api/axes_api.html?#matplotlib.axes.Axes.set_yticklabels)\n",
      "\n",
      "\u523b\u5ea6\u73b0\u5728\u653e\u5728\u4e86\u6b63\u786e\u7684\u4f4d\u7f6e\uff0c\u4f46\u662f\u6807\u7b7e\u5e76\u4e0d\u662f\u663e\u800c\u6613\u89c1\u3002\u6211\u4eec\u80fd\u60f3\u52303.14\u662f\u03c0\uff0c\u4f46\u662f\u6700\u597d\u8ba9\u5b83\u66f4\u660e\u786e\u3002\n",
      "\n",
      "\u5f53\u6211\u4eec\u8bbe\u7f6e\u4e86\u523b\u5ea6\u503c\uff0c\u6211\u4eec\u4e5f\u53ef\u4ee5\u5728\u7b2c\u4e8c\u4e2a\u53c2\u6570\u4e2d\u5217\u51fa\u5bf9\u5e94\u7684\u6807\u7b7e\u3002\u6ce8\u610f\u6211\u4eec\u7528latex\u4ee5\u4fbf\u66f4\u597d\u7684\u6e32\u67d3\u6807\u7b7e\u3002\n",
      "\n",
      "```python\n",
      "pl.xticks([-np.pi, -np.pi/2, 0, np.pi/2, np.pi],\n",
      "          [r'$-\\pi$', r'$-\\pi/2$', r'$0$', r'$+\\pi/2$', r'$+\\pi$'])\n",
      "\n",
      "pl.yticks([-1, 0, +1],\n",
      "          [r'$-1$', r'$0$', r'$+1$'])\n",
      "```\n",
      "\n",
      "### 1.4.2.7 \u79fb\u52a8\u810a\u67f1\n",
      "\n",
      "**\u63d0\u793a**\uff1a\u6587\u6863\n",
      "* [\u810a\u67f1](http://matplotlib.sourceforge.net/api/spines_api.html#matplotlib.spines)\n",
      "* [\u5750\u6807\u8f74\u5bb9\u5668](http://matplotlib.sourceforge.net/users/artists.html#axis-container)\n",
      "* [\u8f6c\u6362\u6559\u7a0b](http://matplotlib.sourceforge.net/users/transforms_tutorial.html)\n",
      "\n",
      "\u810a\u67f1\u662f\u8fde\u63a5\u5750\u6807\u8f74\u523b\u5ea6\u6807\u8bb0\u7684\u7ebf\uff0c\u8bb0\u5f55\u4e86\u6570\u636e\u8303\u56f4\u7684\u8fb9\u754c\u3002\u4ed6\u4eec\u53ef\u4ee5\u88ab\u653e\u5728\u4efb\u610f\u7684\u4f4d\u7f6e\uff0c\u5230\u76ee\u524d\u4f4d\u7f6e\uff0c\u4ed6\u4eec\u88ab\u653e\u5728\u4e86\u5750\u6807\u8f74\u7684\u56db\u5468\u3002\u6211\u4eec\u5c06\u6539\u53d8\u4ed6\u4eec\uff0c\u56e0\u4e3a\u6211\u4eec\u5e0c\u671b\u4ed6\u4eec\u5728\u4e2d\u95f4\u3002\u56e0\u4e3a\u6709\u56db\u6761\uff08\u4e0a\u4e0b\u5de6\u53f3\uff09\uff0c\u6211\u4eec\u901a\u8fc7\u8bbe\u7f6e\u989c\u8272\u4e3aNone\u820d\u5f03\u4e86\u9876\u90e8\u548c\u53f3\u4fa7\uff0c\u5e76\u4e14\u6211\u4eec\u5c06\u628a\u5e95\u90e8\u548c\u5de6\u4fa7\u7684\u810a\u67f1\u79fb\u52a8\u5230\u6570\u636e\u7a7a\u95f4\u5750\u6807\u7684\u96f6\u70b9\u3002\n",
      "\n",
      "```python\n",
      "ax = pl.gca()  # gca stands for 'get current axis'\n",
      "ax.spines['right'].set_color('none')\n",
      "ax.spines['top'].set_color('none')\n",
      "ax.xaxis.set_ticks_position('bottom')\n",
      "ax.spines['bottom'].set_position(('data',0))\n",
      "ax.yaxis.set_ticks_position('left')\n",
      "ax.spines['left'].set_position(('data',0))\n",
      "```\n",
      "\n",
      "<a class=\"reference external image-reference\" href=\"http://scipy-lectures.github.io/intro/matplotlib/auto_examples/plot_exercice_7.html\"><img alt=\"plot_exercice_7_1.png\" class=\"align-right\" src=\"http://scipy-lectures.github.io/_images/plot_exercice_7_1.png\" style=\"width: 280.0px; height: 175.0px;\"></a>\n",
      "\n",
      "### 1.4.2.8 \u6dfb\u52a0\u56fe\u4f8b\n",
      "\n",
      "**\u63d0\u793a**\uff1a\u6587\u6863\n",
      "* [\u56fe\u4f8b\u6307\u5357](http://matplotlib.sourceforge.net/users/legend_guide.html)\n",
      "* [legend()\u547d\u4ee4](http://matplotlib.sourceforge.net/api/pyplot_api.html#matplotlib.pyplot.legend)\n",
      "* [\u56fe\u4f8bAPI](http://matplotlib.sourceforge.net/api/legend_api.html#matplotlib.legend.Legend)\n",
      "\n",
      "\u8ba9\u6211\u4eec\u5728\u5750\u4e0a\u89d2\u6dfb\u52a0\u56fe\u4f8b\u3002\u8fd9\u53ea\u9700\u8981\u5728plot\u547d\u91cc\u4e2d\u6dfb\u52a0\u5173\u952e\u8bcd\u53c2\u6570label\uff08\u5c06\u88ab\u7528\u4e8e\u56fe\u4f8b\u6846\uff09\u3002\n",
      "\n",
      "```python\n",
      "pl.plot(X, C, color=\"blue\", linewidth=2.5, linestyle=\"-\", label=\"cosine\")\n",
      "pl.plot(X, S, color=\"red\",  linewidth=2.5, linestyle=\"-\", label=\"sine\")\n",
      "\n",
      "pl.legend(loc='upper left')\n",
      "```\n",
      "\n",
      "<a class=\"reference external image-reference\" href=\"http://scipy-lectures.github.io/intro/matplotlib/auto_examples/plot_exercice_8.html\"><img alt=\"../../_images/plot_exercice_8_1.png\" class=\"align-right\" src=\"http://scipy-lectures.github.io/_images/plot_exercice_8_1.png\" style=\"width: 280.0px; height: 175.0px;\"></a>\n",
      "\n",
      "### 1.4.2.9 \u6807\u6ce8\u4e00\u4e9b\u70b9\n",
      "\n",
      "**\u63d0\u793a**\uff1a\u6587\u6863\n",
      "* [\u6ce8\u91ca\u5750\u6807\u8f74](http://matplotlib.sourceforge.net/users/annotations_guide.html)\n",
      "* [annotate()\u547d\u4ee4](http://matplotlib.sourceforge.net/api/pyplot_api.html#matplotlib.pyplot.annotate)\n",
      "\n",
      "\u8ba9\u6211\u4eec\u7528annotate\u547d\u4ee4\u6807\u6ce8\u4e00\u4e9b\u6709\u8da3\u7684\u70b9\u3002\u6211\u4eec\u9009\u53d6\u503c2\u03c0/3\uff0c\u6211\u4eec\u60f3\u8981\u6807\u6ce8sine\u548ccosine\u3002\u9996\u5148\u6211\u4eec\u5728\u66f2\u7ebf\u4e0a\u753b\u51fa\u4e86\u4e00\u4e2a\u5782\u76f4\u7684\u6563\u70b9\u6807\u8bb0\u7ebf\u3002\u7136\u540e\uff0c\u6211\u4eec\u5c06\u7528annotate\u547d\u4ee4\u663e\u793a\u5e26\u6709\u526a\u5934\u7684\u6587\u5b57\u3002\n",
      "\n",
      "```python\n",
      "t = 2 * np.pi / 3\n",
      "pl.plot([t, t], [0, np.cos(t)], color='blue', linewidth=2.5, linestyle=\"--\")\n",
      "pl.scatter([t, ], [np.cos(t), ], 50, color='blue')\n",
      "\n",
      "pl.annotate(r'$sin(\\frac{2\\pi}{3})=\\frac{\\sqrt{3}}{2}$',\n",
      "            xy=(t, np.sin(t)), xycoords='data',\n",
      "            xytext=(+10, +30), textcoords='offset points', fontsize=16,\n",
      "            arrowprops=dict(arrowstyle=\"->\", connectionstyle=\"arc3,rad=.2\"))\n",
      "\n",
      "pl.plot([t, t],[0, np.sin(t)], color='red', linewidth=2.5, linestyle=\"--\")\n",
      "pl.scatter([t, ],[np.sin(t), ], 50, color='red')\n",
      "\n",
      "pl.annotate(r'$cos(\\frac{2\\pi}{3})=-\\frac{1}{2}$',\n",
      "            xy=(t, np.cos(t)), xycoords='data',\n",
      "            xytext=(-90, -50), textcoords='offset points', fontsize=16,\n",
      "            arrowprops=dict(arrowstyle=\"->\", connectionstyle=\"arc3,rad=.2\"))\n",
      "```\n",
      "\n",
      "<a class=\"reference external image-reference\" href=\"http://scipy-lectures.github.io/intro/matplotlib/auto_examples/plot_exercice_9.html\"><img alt=\"plot_exercice_9_1.png\" class=\"align-right\" src=\"http://scipy-lectures.github.io/_images/plot_exercice_9_1.png\" style=\"width: 280.0px; height: 175.0px;\"></a>\n",
      "\n",
      "### 1.4.2.10 \u7ec6\u8282\u662f\u9b54\u9b3c\n",
      "\n",
      "**\u63d0\u793a**\uff1a\u6587\u6863\n",
      "* [Artists](http://matplotlib.sourceforge.net/api/artist_api.html)\n",
      "* [BBox](http://matplotlib.sourceforge.net/api/artist_api.html#matplotlib.text.Text.set_bbox)\n",
      "\n",
      "\u56e0\u4e3a\u84dd\u8272\u548c\u7ea2\u8272\u7684\u7ebf\uff0c\u523b\u5ea6\u6807\u7b7e\u5f88\u96be\u770b\u5230\u3002\u6211\u4eec\u53ef\u4ee5\u8ba9\u4ed6\u4eec\u66f4\u5927\u4e00\u4e9b\uff0c\u4e5f\u53ef\u4ee5\u8c03\u6574\u4ed6\u4eec\u7684\u5c5e\u6027\u4ee5\u4fbf\u4ed6\u4eec\u88ab\u5904\u7406\u4e3a\u534a\u900f\u660e\u7684\u767d\u8272\u80cc\u666f\u3002\u8fd9\u6837\u6211\u4eec\u5c31\u53ef\u4ee5\u540c\u65f6\u770b\u5230\u6570\u636e\u548c\u6807\u7b7e\u3002\n",
      "\n",
      "```python\n",
      "for label in ax.get_xticklabels() + ax.get_yticklabels():\n",
      "    label.set_fontsize(16)\n",
      "    label.set_bbox(dict(facecolor='white', edgecolor='None', alpha=0.65))\n",
      "```\n",
      "\n",
      "<a class=\"reference external image-reference\" href=\"http://scipy-lectures.github.io/intro/auto_examples/plot_exercice_10.html\"><img alt=\"plot_exercice_10_1.png\" class=\"align-right\" src=\"http://scipy-lectures.github.io/_images/plot_exercice_10_1.png\" style=\"width: 280.0px; height: 175.0px;\"></a>\n",
      "\n",
      "## 1.4.3 \u56fe\u5f62\u3001\u5b50\u56fe\u3001\u8f74\u548c\u523b\u5ea6\n",
      "\n",
      "\u5728matplotlib\u4e2d\u201c**\u56fe\u5f62**\u201d\u662f\u7528\u6237\u754c\u9762\u4e2d\u7684\u6574\u4e2a\u7a97\u53e3\u3002\u5728\u8fd9\u4e2a\u56fe\u5f62\u4e2d\u53ef\u4ee5\u6709\u201c**\u5b50\u56fe**\u201d\u3002\n",
      "\n",
      "\u5230\u76ee\u524d\u4e3a\u6b62\uff0c\u6211\u4eec\u5df2\u7ecf\u4f7f\u7528\u56fe\u5f62\u548c\u521b\u5efa\u6570\u8f74\u3002\u8fd9\u5bf9\u4e8e\u5feb\u901f\u7ed8\u56fe\u662f\u975e\u5e38\u65b9\u4fbf\u7684\u3002\u4f7f\u7528\u56fe\u5f62\u3001\u5b50\u56fe\u548c\u8f74\u6211\u4eec\u53ef\u4ee5\u63a7\u5236\u663e\u793a\u3002\u5c3d\u7ba1\u5b50\u56fe\u5c06\u56fe\u8868\u653e\u5728\u6807\u51c6\u7684\u7f51\u683c\u4e2d\uff0c\u8f74\u53ef\u4ee5\u5728\u56fe\u5f62\u4e2d\u653e\u5728\u4efb\u610f\u4f4d\u7f6e\u3002\u6839\u636e\u4f60\u7684\u76ee\u7684\u4e0d\u540c\uff0c\u4e8c\u8005\u90fd\u975e\u5e38\u6709\u7528\u3002\u6211\u4eec\u4e5f\u5728\u6ca1\u6709\u663e\u5f0f\u7684\u8c03\u7528\u56fe\u5f62\u548c\u5b50\u56fe\u65f6\u4f7f\u7528\u4e86\u4ed6\u4eec\u3002\u5f53\u6211\u4eec\u8c03\u7528plot\u65f6\uff0cmatplotlib\u8c03\u7528`gca()`\u6765\u83b7\u5f97\u5f53\u524d\u7684\u5750\u6807\u8f74\uff0c\u76f8\u5e94\u7684\u8c03\u7528`gcf()`\u83b7\u5f97\u5f53\u524d\u7684\u56fe\u5f62\u3002\u5982\u679c\u6ca1\u6709\u5f53\u524d\u56fe\u5f62\uff0c\u90a3\u4e48\u5c06\u8c03\u7528`figure()`\u53bb\u521b\u5efa\u4e00\u4e2a\uff0c\u4e25\u683c\u6765\u8bf4\u662f\u521b\u5efa\u4e00\u4e2a`subplot(111)\u3002\u8ba9\u6211\u4eec\u6765\u8be6\u7ec6\u770b\u4e00\u4e0b\u3002\n",
      "\n",
      "### 1.4.3.1 \u56fe\u5f62\n",
      "\n",
      "\u56fe\u5f62\u662f\u5728GUI\u4e2d\u7684\u7a97\u53e3\uff0c\u6807\u9898\u662f\"Figure #\"\u3002\u56fe\u5f62\u7684\u6807\u53f7\u4ece1\u5f00\u59cb\uff0c\u800c\u4e0d\u662f\u5e38\u89c4\u7684Python\u65b9\u5f0f\u4ece0\u5f00\u59cb\u3002\u8fd9\u660e\u663e\u662fMATLAB-\u98ce\u683c\u3002\u8fd9\u4e9b\u53c2\u6570\u51b3\u5b9a\u56fe\u5f62\u7684\u5916\u89c2\uff1a\n",
      "\n",
      "\u53c2\u6570|\u9ed8\u8ba4\u503c|\u63cf\u8ff0\n",
      "--------|-------|-----------\n",
      "num     |1      |\u56fe\u5f62\u7f16\u53f7\n",
      "figsize |figure.figsize|\u4ee5\u82f1\u5bf8\u8868\u793a\u7684\u56fe\u5f62\u5927\u5c0f\uff08\u5bbd\u3001\u9ad8\uff09\n",
      "dpi     |figure.dpi|\u5206\u8fa8\u7387\u4ee5\u6bcf\u82f1\u5bf8\u70b9\u6570\u8868\u793a\n",
      "facecolor|figure.facecolor|\u80cc\u666f\u8272\n",
      "edgecolor|figure.edgecolor|\u80cc\u666f\u8fb9\u7f18\u8272\n",
      "frameon|True|\u662f\u5426\u7ed8\u5236\u6846\u67b6\n",
      "\n",
      "\u9ed8\u8ba4\u503c\u53ef\u4ee5\u5728\u8d44\u6e90\u6587\u4ef6\u4e2d\u6307\u660e\uff0c\u5e76\u5728\u7edd\u5927\u6570\u65f6\u95f4\u4f7f\u7528\u3002\u53ea\u6709\u56fe\u5f62\u6570\u7ecf\u5e38\u88ab\u6539\u53d8\u3002\n",
      "\n",
      "\u4e0e\u5176\u4ed6\u5bf9\u8c61\u7c7b\u4f3c\uff0c\u4f60\u53ef\u4ee5\u7528setp\u6216\u8005set_something\u65b9\u6cd5\u8bbe\u7f6e\u56fe\u5f62\u5c5e\u6027\u3002\n",
      "\n",
      "\u5f53\u4f60\u4f7f\u7528GUI\u5de5\u4f5c\u65f6\uff0c\u4f60\u53ef\u4ee5\u70b9\u51fb\u53f3\u4e0a\u7684X\u5173\u95ed\u56fe\u5f62\u3002\u4f46\u662f\uff0c\u4f60\u53ef\u4ee5\u901a\u8fc7\u8c03\u7528close\u7528\u7a0b\u5e8f\u5173\u95ed\u56fe\u5f62\u3002\u6839\u636e\u53c2\u6570\u5173\u95ed\u4e0d\u540c\u5185\u5bb9\uff081\uff09\u5f53\u524d\u56fe\u5f62\uff08\u6ca1\u6709\u53c2\u6570\uff09\uff0c\uff082\uff09\u7279\u5b9a\u56fe\u5f62\uff08\u7528\u56fe\u5f62\u7f16\u53f7\u6216\u56fe\u5f62\u5b9e\u4f8b\u505a\u53c2\u6570\uff09\uff0c\uff083\uff09\u6240\u6709\u56fe\u5f62\uff08\"all\"\u4f5c\u4e3a\u53c2\u6570\uff09\u3002\n",
      "\n",
      "```python\n",
      "pl.close(1)     # Closes figure 1\n",
      "```\n",
      "\n",
      "### 1.4.3.2 \u5b50\u56fe\n",
      "\n",
      "\u7528\u5b50\u56fe\u4f60\u53ef\u4ee5\u5c06\u56fe\u7247\u653e\u7f6e\u5728\u6807\u51c6\u65b9\u683c\u4e2d\u3002\u4f60\u9700\u8981\u6307\u5b9a\u884c\u5217\u6570\u548c\u56fe\u7247\u6570\u3002 \u6ce8\u610f[gridspec](http://matplotlib.org/users/gridspec.html)\u547d\u4ee4\u76f8\u5bf9\u66f4\u52a0\u9ad8\u7ea7\u3002\n",
      "\n",
      "<a class=\"reference external image-reference\" href=\"http://scipy-lectures.github.io/intro/auto_examples/plot_subplot-horizontal.html\"><img alt=\"plot_subplot-horizontal_1.png\" src=\"http://scipy-lectures.github.io/_images/plot_subplot-horizontal_1.png\" style=\"width: 168.0px; height: 112.0px;\"></a><a class=\"reference external image-reference\" href=\"http://scipy-lectures.github.io/intro/auto_examples/plot_subplot-vertical.html\"><img alt=\"plot_subplot-vertical_1.png\" src=\"http://scipy-lectures.github.io/_images/plot_subplot-vertical_1.png\" style=\"width: 168.0px; height: 112.0px;\"></a><a class=\"reference external image-reference\" href=\"http://scipy-lectures.github.io/intro/auto_examples/plot_subplot-grid.html\"><img alt=\"plot_subplot-grid_1.png\" src=\"http://scipy-lectures.github.io/_images/plot_subplot-grid_1.png\" style=\"width: 168.0px; height: 112.0px;\"></a><a class=\"reference external image-reference\" href=\"http://scipy-lectures.github.io/intro/auto_examples/plot_gridspec.html\"><img alt=\"plot_gridspec_1.png\" src=\"http://scipy-lectures.github.io/_images/plot_gridspec_1.png\" style=\"width: 168.0px; height: 112.0px;\"></a>\n",
      "\n",
      "\n",
      "### 1.4.3.3 \u8f74\n",
      "\n",
      "\u8f74\u4e0e\u5b50\u56fe\u975e\u5e38\u7c7b\u4f3c\uff0c\u4e0d\u8fc7\u5141\u8bb8\u56fe\u5f62\u653e\u5728\u56fe\u7247\u7684\u4efb\u610f\u4f4d\u7f6e\u3002\u56e0\u6b64\uff0c\u5982\u679c\u6211\u4eec\u60f3\u8981\u5c06\u4e00\u4e2a\u5c0f\u56fe\u5f62\u653e\u5728\u4e00\u4e2a\u66f4\u5927\u56fe\u5f62\u4e2d\uff0c\u6211\u4eec\u53ef\u4ee5\u7528\u8f74\u3002\n",
      "\n",
      "<a class=\"reference external image-reference\" href=\"http://scipy-lectures.github.io/intro/auto_examples/plot_axes.html\"><img alt=\"plot_axes_1.png\" src=\"http://scipy-lectures.github.io/_images/plot_axes_1.png\" style=\"width: 280.0px; height: 210.0px;\"></a><a class=\"reference external image-reference\" href=\"http://scipy-lectures.github.io/intro/auto_examples/plot_axes-2.html\"><img alt=\"plot_axes-2_1.png\" src=\"http://scipy-lectures.github.io/_images/plot_axes-2_1.png\" style=\"width: 280.0px; height: 210.0px;\"></a>\n",
      "\n",
      "### 1.4.3.4 \u523b\u5ea6\n",
      "\n",
      "\u683c\u5f0f\u826f\u597d\u7684\u523b\u5ea6\u662f\u51c6\u5907\u597d\u53d1\u5e03\u56fe\u7247\u7684\u5fc5\u8981\u90e8\u5206\u3002Matplotlib\u63d0\u4f9b\u4e86\u4e00\u4e2a\u5b8c\u5168\u53ef\u63a7\u7684\u523b\u5ea6\u7cfb\u7edf\u3002\u6709\u523b\u5ea6\u4f4d\u7f6e\u6765\u6307\u5b9a\u523b\u5ea6\u8be5\u51fa\u73b0\u5728\u54ea\uff0c\u8fd8\u6709\u523b\u5ea6\u683c\u5f0f\u6765\u7ed9\u51fa\u4f60\u60f3\u8981\u7684\u523b\u5ea6\u5916\u89c2\u3002\u4e3b\u523b\u5ea6\u548c\u5b50\u523b\u5ea6\u53ef\u4ee5\u88ab\u72ec\u7acb\u653e\u7f6e\u548c\u6574\u7406\u683c\u5f0f\u3002\u4e4b\u524d\u5b50\u523b\u5ea6\u9ed8\u8ba4\u662f\u4e0d\u663e\u793a\u7684\uff0c\u5373\u4ed6\u4eec\u53ea\u6709\u7a7a\u5217\u8868\uff0c\u56e0\u4e3a\u5b83\u662f`NullLocator` (\u89c1\u4e0b\u9762)\u3002\n",
      "\n",
      "#### 1.4.3.4.1 \u523b\u5ea6\u4f4d\u7f6e\n",
      "\n",
      "\u523b\u5ea6\u4f4d\u7f6e\u53ef\u4ee5\u63a7\u5236\u7684\u4f4d\u7f6e\u3002\u5b83\u7684\u8bbe\u7f6e\u5982\u4e0b\uff1a\n",
      "\n",
      "```python\n",
      "ax = pl.gca()\n",
      "ax.xaxis.set_major_locator(eval(locator))\n",
      "```\n",
      "\u4e0d\u540c\u7684\u9700\u6c42\u6709\u591a\u79cd\u4f4d\u7f6e\uff1a\n",
      "\n",
      "<a class=\"reference external image-reference\" href=\"http://scipy-lectures.github.io/intro/auto_examples/plot_ticks.html\"><img alt=\"plot_ticks_1.png\" src=\"http://scipy-lectures.github.io/_images/plot_ticks_1.png\" style=\"width: 426.6px; height: 232.8px;\"></a>\n",
      "\n",
      "\u6240\u6709\u8fd9\u4e9b\u4f4d\u7f6e\u90fd\u53ef\u4ee5\u4ece\u57fa\u7840\u7c7b[matplotlib.ticker.Locator](http://matplotlib.org/api/ticker_api.html#matplotlib.ticker.Locator)\u884d\u751f\u51fa\u6765\u3002\u4f60\u53ef\u4ee5\u4ece\u4e2d\u884d\u751f\u51fa\u4f60\u81ea\u5df1\u7684\u4f4d\u7f6e\u3002\u5c06\u65e5\u671f\u5904\u7406\u4e3a\u523b\u5ea6\u7279\u522b\u56f0\u54ea\u3002\u56e0\u6b64\uff0c`matplotlib`\u63d0\u4f9b\u4e86\u7279\u6b8a\u7684\u4f4d\u7f6e`matplotlib.dates`\u3002\n",
      "\n",
      "## 1.4.4 \u5176\u4ed6\u7c7b\u578b\u7684\u56fe\u5f62\uff1a\u4f8b\u5b50\u4e0e\u7ec3\u4e60\n",
      "\n",
      "### 1.4.4.1 \u5e38\u89c4\u56fe\u5f62\n",
      "\n",
      "---\n",
      "**\u63d0\u793a\uff1a**\u4f60\u53ef\u4ee5\u4f7f\u7528[fill_between](http://matplotlib.sourceforge.net/api/pyplot_api.html#matplotlib.pyplot.fill_between)\u547d\u4ee4\u3002\n",
      "\n",
      "---\n",
      "\n",
      "<a class=\"reference external image-reference\" href=\"http://scipy-lectures.github.io/intro/auto_examples/plot_plot_ex.html\"><img alt=\"plot_plot_ex_1.png\" class=\"align-right\" src=\"http://scipy-lectures.github.io/_images/plot_plot_ex_1.png\" style=\"width: 280.0px; height: 210.0px;\"></a>\n",
      "\n",
      "\u4ece\u4e0b\u9762\u7684\u4ee3\u7801\u5f00\u59cb\uff0c\u8bd5\u7740\u91cd\u65b0\u751f\u6210\u8fd9\u4e2a\u56fe\u7247\uff0c\u5c0f\u5fc3\u5904\u7406\u586b\u5145\u533a\u57df\uff1a\n",
      "\n",
      "```python\n",
      "n = 256\n",
      "X = np.linspace(-np.pi, np.pi, n, endpoint=True)\n",
      "Y = np.sin(2 * X)\n",
      "\n",
      "pl.plot(X, Y + 1, color='blue', alpha=1.00)\n",
      "pl.plot(X, Y - 1, color='blue', alpha=1.00)\n",
      "```\n",
      "\u70b9\u51fb\u56fe\u7247\u67e5\u770b\u7b54\u6848\u3002\n",
      "\n",
      "### 1.4.4.2 \u6563\u70b9\u56fe\n",
      "\n",
      "---\n",
      "**\u63d0\u793a\uff1a**\u989c\u8272\u6839\u636e\u89d2\u5ea6\u8fdb\u884c\u5206\u914d\n",
      "\n",
      "---\n",
      "\n",
      "<a class=\"reference external image-reference\" href=\"http://scipy-lectures.github.io/intro/auto_examples/plot_scatter_ex.html\"><img alt=\"plot_scatter_ex_1.png\" class=\"align-right\" src=\"http://scipy-lectures.github.io/_images/plot_scatter_ex_1.png\" style=\"width: 280.0px; height: 210.0px;\"></a>\n",
      "\n",
      "\n",
      "\u4ece\u4e0b\u9762\u7684\u4ee3\u7801\u5f00\u59cb\uff0c\u8bd5\u7740\u91cd\u65b0\u751f\u6210\u8fd9\u4e2a\u56fe\u7247\uff0c\u5c0f\u5fc3\u5904\u7406\u6807\u8bb0\u7684\u5927\u5c0f\u989c\u8272\u548c\u900f\u660e\u5ea6\uff1a\n",
      "\n",
      "```python\n",
      "n = 1024\n",
      "X = np.random.normal(0,1,n)\n",
      "Y = np.random.normal(0,1,n)\n",
      "\n",
      "pl.scatter(X,Y)\n",
      "```\n",
      "\u70b9\u51fb\u56fe\u7247\u67e5\u770b\u7b54\u6848\u3002\n",
      "\n",
      "### 1.4.4.3 \u67f1\u72b6\u56fe\n",
      "\n",
      "---\n",
      "**\u63d0\u793a\uff1a**\u4f60\u9700\u8981\u5c0f\u5fc3\u6587\u672c\u5bf9\u9f50\n",
      "\n",
      "---\n",
      "<a class=\"reference external image-reference\" href=\"http://scipy-lectures.github.io/intro/auto_examples/plot_bar_ex.html\"><img alt=\"plot_bar_ex_1.png\" class=\"align-right\" src=\"http://scipy-lectures.github.io/_images/plot_bar_ex_1.png\" style=\"width: 280.0px; height: 210.0px;\"></a>\n",
      "\n",
      "\u4ece\u4e0b\u9762\u7684\u4ee3\u7801\u5f00\u59cb\uff0c\u8bd5\u7740\u91cd\u65b0\u751f\u6210\u8fd9\u4e2a\u56fe\u7247\uff0c\u6dfb\u52a0\u7ea2\u67f1\u7684\u6807\u7b7e\u3002\n",
      "\n",
      "```python\n",
      "n = 12\n",
      "X = np.arange(n)\n",
      "Y1 = (1 - X / float(n)) * np.random.uniform(0.5, 1.0, n)\n",
      "Y2 = (1 - X / float(n)) * np.random.uniform(0.5, 1.0, n)\n",
      "\n",
      "pl.bar(X, +Y1, facecolor='#9999ff', edgecolor='white')\n",
      "pl.bar(X, -Y2, facecolor='#ff9999', edgecolor='white')\n",
      "\n",
      "for x, y in zip(X, Y1):\n",
      "    pl.text(x + 0.4, y + 0.05, '%.2f' % y, ha='center', va='bottom')\n",
      "\n",
      "pl.ylim(-1.25, +1.25)\n",
      "```\n",
      "\u70b9\u51fb\u56fe\u7247\u67e5\u770b\u7b54\u6848\u3002\n",
      "\n",
      "### 1.4.4.4 \u8f6e\u5ed3\u56fe\n",
      "\n",
      "---\n",
      "**\u63d0\u793a\uff1a**\u4f60\u9700\u8981\u662f\u4f7f\u7528[clabel](http://matplotlib.sourceforge.net/api/pyplot_api.html#matplotlib.pyplot.clabel)\u547d\u4ee4\u3002\n",
      "\n",
      "---\n",
      "\n",
      "<a class=\"reference external image-reference\" href=\"http://scipy-lectures.github.io/intro/auto_examples/plot_contour_ex.html\"><img alt=\"plot_contour_ex_1.png\" class=\"align-right\" src=\"http://scipy-lectures.github.io/_images/plot_contour_ex_1.png\" style=\"width: 280.0px; height: 210.0px;\"></a>\n",
      "\n",
      "\u4ece\u4e0b\u9762\u7684\u4ee3\u7801\u5f00\u59cb\uff0c\u8bd5\u7740\u91cd\u65b0\u751f\u6210\u8fd9\u4e2a\u56fe\u7247\uff0c\u5c0f\u5fc3\u5904\u7406colormap (\u89c1\u4e0b\u9762\u7684[Colormaps](http://scipy-lectures.github.io/intro/matplotlib/matplotlib.html#colormaps))\u3002\n",
      "\n",
      "```python\n",
      "def f(x, y):\n",
      "    return (1 - x / 2 + x ** 5 + y ** 3) * np.exp(-x ** 2 -y ** 2)\n",
      "\n",
      "n = 256\n",
      "x = np.linspace(-3, 3, n)\n",
      "y = np.linspace(-3, 3, n)\n",
      "X, Y = np.meshgrid(x, y)\n",
      "\n",
      "pl.contourf(X, Y, f(X, Y), 8, alpha=.75, cmap='jet')\n",
      "C = pl.contour(X, Y, f(X, Y), 8, colors='black', linewidth=.5)\n",
      "```\n",
      "\u70b9\u51fb\u56fe\u7247\u67e5\u770b\u7b54\u6848\u3002\n",
      "\n",
      "### 1.4.4.5 Imshow\n",
      "\n",
      "---\n",
      "**\u63d0\u793a\uff1a**\u4f60\u9700\u8981\u5c0f\u5fc3\u5904\u7406\u5728imshow\u547d\u4ee4\u4e2d\u7684\u56fe\u50cf*\u539f\u70b9*\u5e76\u4f7f\u7528[colorbar](http://matplotlib.sourceforge.net/api/pyplot_api.html#matplotlib.pyplot.colorbar)\n",
      "\n",
      "---\n",
      "\n",
      "<a class=\"reference external image-reference\" href=\"http://scipy-lectures.github.io/intro/auto_examples/plot_imshow_ex.html\"><img alt=\"plot_imshow_ex_1.png\" class=\"align-right\" src=\"http://scipy-lectures.github.io/_images/plot_imshow_ex_1.png\" style=\"width: 280.0px; height: 210.0px;\"></a>\n",
      "\n",
      "\u4ece\u4e0b\u9762\u7684\u4ee3\u7801\u5f00\u59cb\uff0c\u8bd5\u7740\u91cd\u65b0\u751f\u6210\u8fd9\u4e2a\u56fe\u7247\uff0c\u5c0f\u5fc3\u5904\u7406colormap\u548c\u56fe\u50cf\u63d2\u5165\u4ee5\u53ca\u539f\u70b9\u3002\n",
      "\n",
      "```python\n",
      "def f(x, y):\n",
      "    return (1 - x / 2 + x ** 5 + y ** 3) * np.exp(-x ** 2 - y ** 2)\n",
      "\n",
      "n = 10\n",
      "x = np.linspace(-3, 3, 4 * n)\n",
      "y = np.linspace(-3, 3, 3 * n)\n",
      "X, Y = np.meshgrid(x, y)\n",
      "pl.imshow(f(X, Y))\n",
      "```\n",
      "\u70b9\u51fb\u56fe\u7247\u67e5\u770b\u7b54\u6848\u3002\n",
      "\n",
      "### 1.4.4.6 \u997c\u56fe\n",
      "\n",
      "---\n",
      "**\u63d0\u793a\uff1a**\u4f60\u9700\u8981\u8c03\u6574Z\u3002\n",
      "\n",
      "---\n",
      "\n",
      "<a class=\"reference external image-reference\" href=\"http://scipy-lectures.github.io/intro/auto_examples/plot_pie_ex.html\"><img alt=\"plot_pie_ex_1.png\" class=\"align-right\" src=\"http://scipy-lectures.github.io/_images/plot_pie_ex_1.png\" style=\"width: 280.0px; height: 210.0px;\"></a>\n",
      "\n",
      "\u4ece\u4e0b\u9762\u7684\u4ee3\u7801\u5f00\u59cb\uff0c\u8bd5\u7740\u91cd\u65b0\u751f\u6210\u8fd9\u4e2a\u56fe\u7247\uff0c\u5c0f\u5fc3\u5904\u7406\u989c\u8272\u548c\u5207\u7247\u5927\u5c0f\u3002\n",
      "\n",
      "```python\n",
      "Z = np.random.uniform(0, 1, 20)\n",
      "pl.pie(Z)\n",
      "```\n",
      "\u70b9\u51fb\u56fe\u7247\u67e5\u770b\u7b54\u6848\u3002\n",
      "\n",
      "### 1.4.4.7 \u68af\u5ea6\u56fe\n",
      "\n",
      "---\n",
      "**\u63d0\u793a\uff1a**\u4f60\u9700\u8981\u7ed8\u5236\u4e24\u6b21\u7bad\u5934\u3002\n",
      "\n",
      "---\n",
      "\n",
      "<a class=\"reference external image-reference\" href=\"http://scipy-lectures.github.io/intro/auto_examples/plot_quiver_ex.html\"><img alt=\"plot_quiver_ex_1.png\" class=\"align-right\" src=\"http://scipy-lectures.github.io/_images/plot_quiver_ex_1.png\" style=\"width: 280.0px; height: 210.0px;\"></a>\n",
      "\n",
      "\u4ece\u4e0b\u9762\u7684\u4ee3\u7801\u5f00\u59cb\uff0c\u8bd5\u7740\u91cd\u65b0\u751f\u6210\u8fd9\u4e2a\u56fe\u7247\uff0c\u5c0f\u5fc3\u5904\u7406\u989c\u8272\u548c\u65b9\u5411\u3002\n",
      "\n",
      "```python\n",
      "n = 8\n",
      "X, Y = np.mgrid[0:n, 0:n]\n",
      "pl.quiver(X, Y)\n",
      "```\n",
      "\u70b9\u51fb\u56fe\u7247\u67e5\u770b\u7b54\u6848\u3002\n",
      "\n",
      "### 1.4.4.8 \u7f51\u683c\n",
      "\n",
      "<a class=\"reference external image-reference\" href=\"http://scipy-lectures.github.io/intro/auto_examples/plot_grid_ex.html\"><img alt=\"plot_grid_ex_1.png\" class=\"align-right\" src=\"http://scipy-lectures.github.io/_images/plot_grid_ex_1.png\" style=\"width: 280.0px; height: 210.0px;\"></a>\n",
      "\n",
      "\u4ece\u4e0b\u9762\u7684\u4ee3\u7801\u5f00\u59cb\uff0c\u8bd5\u7740\u91cd\u65b0\u751f\u6210\u8fd9\u4e2a\u56fe\u7247\uff0c\u5c0f\u5fc3\u5904\u7406\u7ebf\u7684\u6837\u5f0f\u3002\n",
      "\n",
      "```python\n",
      "axes = pl.gca()\n",
      "axes.set_xlim(0, 4)\n",
      "axes.set_ylim(0, 3)\n",
      "axes.set_xticklabels([])\n",
      "axes.set_yticklabels([])\n",
      "```\n",
      "\u70b9\u51fb\u56fe\u7247\u67e5\u770b\u7b54\u6848\u3002\n",
      "\n",
      "### 1.4.4.9 \u591a\u56fe\n",
      "\n",
      "---\n",
      "**\u63d0\u793a\uff1a**\u4f60\u53ef\u4ee5\u7528\u4e0d\u540c\u7684\u5206\u5272\u6765\u4f7f\u7528\u591a\u4e2a\u5b50\u56fe\u3002\n",
      "\n",
      "---\n",
      "\n",
      "<a class=\"reference external image-reference\" href=\"http://scipy-lectures.github.io/intro/auto_examples/plot_multiplot_ex.html\"><img alt=\"plot_multiplot_ex_1.png\" class=\"align-right\" src=\"http://scipy-lectures.github.io/_images/plot_multiplot_ex_1.png\" style=\"width: 280.0px; height: 210.0px;\"></a>\n",
      "\n",
      "```python\n",
      "pl.subplot(2, 2, 1)\n",
      "pl.subplot(2, 2, 3)\n",
      "pl.subplot(2, 2, 4)\n",
      "```\n",
      "\u70b9\u51fb\u56fe\u7247\u67e5\u770b\u7b54\u6848\u3002\n",
      "\n",
      "### 1.4.4.10 \u6781\u5750\u6807\u7cfb\n",
      "\n",
      "---\n",
      "**\u63d0\u793a\uff1a**\u4f60\u53ea\u9700\u8981\u4fee\u6539`axes`\u884c\u3002\n",
      "\n",
      "---\n",
      "\n",
      "<a class=\"reference external image-reference\" href=\"http://scipy-lectures.github.io/intro/auto_examples/plot_polar_ex.html\"><img alt=\"plot_polar_ex_1.png\" class=\"align-right\" src=\"http://scipy-lectures.github.io/_images/plot_polar_ex_1.png\" style=\"width: 280.0px; height: 210.0px;\"></a>\n",
      " \n",
      "\u4ece\u4e0b\u9762\u7684\u4ee3\u7801\u5f00\u59cb\uff0c\u8bd5\u7740\u91cd\u65b0\u751f\u6210\u8fd9\u4e2a\u56fe\u7247\u3002\n",
      "\n",
      "```python\n",
      "pl.axes([0, 0, 1, 1])\n",
      "\n",
      "N = 20\n",
      "theta = np.arange(0., 2 * np.pi, 2 * np.pi / N)\n",
      "radii = 10 * np.random.rand(N)\n",
      "width = np.pi / 4 * np.random.rand(N)\n",
      "bars = pl.bar(theta, radii, width=width, bottom=0.0)\n",
      "\n",
      "for r, bar in zip(radii, bars):\n",
      "    bar.set_facecolor(cm.jet(r / 10.))\n",
      "    bar.set_alpha(0.5)\n",
      "```\n",
      "\u70b9\u51fb\u56fe\u7247\u67e5\u770b\u7b54\u6848\u3002\n",
      "\n",
      "\n",
      "### 1.4.4.11 3D\u7ed8\u56fe\n",
      "\n",
      "---\n",
      "**\u63d0\u793a\uff1a**\u4f60\u9700\u8981\u4f7f\u7528[contourf](http://matplotlib.sourceforge.net/api/pyplot_api.html#matplotlib.pyplot.contourf)\n",
      "\n",
      "---\n",
      "\n",
      "<a class=\"reference external image-reference\" href=\"http://scipy-lectures.github.io/intro/auto_examples/plot_plot3d_ex.html\"><img alt=\"plot_plot3d_ex_1.png\" class=\"align-right\" src=\"http://scipy-lectures.github.io/_images/plot_plot3d_ex_1.png\" style=\"width: 280.0px; height: 210.0px;\"></a>\n",
      "\n",
      "\u4ece\u4e0b\u9762\u7684\u4ee3\u7801\u5f00\u59cb\uff0c\u8bd5\u7740\u91cd\u65b0\u751f\u6210\u8fd9\u4e2a\u56fe\u7247\u3002\n",
      "\n",
      "```python\n",
      "from mpl_toolkits.mplot3d import Axes3D\n",
      "\n",
      "fig = pl.figure()\n",
      "ax = Axes3D(fig)\n",
      "X = np.arange(-4, 4, 0.25)\n",
      "Y = np.arange(-4, 4, 0.25)\n",
      "X, Y = np.meshgrid(X, Y)\n",
      "R = np.sqrt(X**2 + Y**2)\n",
      "Z = np.sin(R)\n",
      "\n",
      "ax.plot_surface(X, Y, Z, rstride=1, cstride=1, cmap='hot')\n",
      "```\n",
      "\n",
      "\u70b9\u51fb\u56fe\u7247\u67e5\u770b\u7b54\u6848\u3002\n",
      "\n",
      "**\u66f4\u591a\u8bf7\u89c1**\uff1a[\u7528Mayavi 3D\u7ed8\u56fe](http://scipy-lectures.github.io/packages/3d_plotting/index.html#mayavi-label)\n",
      "\n",
      "### 1.4.4.12 \u6587\u672c\n",
      "\n",
      "---\n",
      "**\u63d0\u793a\uff1a**\u770b\u4e00\u4e0b[matplotlib\u6807\u8bc6](http://matplotlib.org/examples/api/logo2.html)\n",
      "\n",
      "---\n",
      "\n",
      "<a class=\"reference external image-reference\" href=\"http://scipy-lectures.github.io/intro/auto_examples/plot_text_ex.html\"><img alt=\"plot_text_ex_1.png\" class=\"align-right\" src=\"http://scipy-lectures.github.io/_images/plot_text_ex_1.png\" style=\"width: 280.0px; height: 210.0px;\"></a>\n",
      "\n",
      "\u8bd5\u7740\u4ece0\u5f00\u59cb\u505a\u8fd9\u4e2a\u4e8b\u60c5\uff01\n",
      "\n",
      "\u70b9\u51fb\u56fe\u7247\u67e5\u770b\u7b54\u6848\u3002\n",
      "\n",
      "---\n",
      "**\u5feb\u901f\u9605\u8bfb**\n",
      "\n",
      "\u5982\u679c\u4f60\u60f3\u8981\u5feb\u901f\u770b\u4e00\u4e0bScipy\u8bb2\u5ea7\u4ee5\u4fbf\u4e86\u89e3\u751f\u6001\u7cfb\u7edf\uff0c\u4f60\u53ef\u4ee5\u76f4\u63a5\u8df3\u5230\u4e0b\u4e00\u7ae0\uff1aScipy\uff1a\u9ad8\u7ea7\u79d1\u5b66\u8ba1\u7b97\u3002\n",
      "\n",
      "\u672c\u7ae0\u7684\u5269\u4f59\u90e8\u5206\u5bf9\u7406\u89e3\u5176\u4ed6\u7684\u4ecb\u7ecd\u90e8\u5206\u4e0d\u662f\u5fc5\u987b\u7684\u3002\u4f46\u662f\uff0c\u8bf7\u786e\u4fdd\u5728\u7a0d\u540e\u56de\u6765\u5b8c\u6210\u8fd9\u4e2a\u7ae0\u8282\u3002\n",
      "\n",
      "## 1.4.5 \u8fd9\u672c\u6559\u7a0b\u4e4b\u5916\n",
      "\n",
      "Matplotlib\u4ece\u5927\u91cf\u7684\u6587\u6863\u4ee5\u53ca\u7528\u6237\u548c\u5f00\u53d1\u8005\u793e\u533a\u4e2d\u6536\u76ca\u532a\u6d45\u3002\u8fd9\u91cc\u662f\u4e00\u4e9b\u6709\u8da3\u7684\u94fe\u63a5\uff1a \n",
      "\n",
      "### 1.4.5.1 \u6559\u7a0b\n",
      "\n",
      "* [Pyplot\u6559\u7a0b](http://matplotlib.sourceforge.net/users/pyplot_tutorial.html)\n",
      "    * \u4ecb\u7ecd\n",
      "    * \u63a7\u5236line\u5c5e\u6027\n",
      "    * \u5904\u7406\u591a\u4e2a\u56fe\u5f62\u548c\u5750\u6807\u8f74\n",
      "    * \u5904\u7406\u6587\u672c\n",
      "* [Image\u6559\u7a0b](http://matplotlib.sourceforge.net/users/image_tutorial.html)\n",
      "    * \u5f00\u59cb\u547d\u4ee4\n",
      "    * \u4eceNumpy\u6570\u7ec4\u4e2d\u5bfc\u5165\u56fe\u50cf\u6570\u636e\n",
      "    * \u5c06numpy\u6570\u7ec4\u7ed8\u5236\u4e3a\u56fe\u50cf\n",
      "* [Text\u6559\u7a0b](http://matplotlib.sourceforge.net/users/index_text.html)\n",
      "    * Text\u4ecb\u7ecd\n",
      "    * \u57fa\u672ctext\u547d\u4ee4\n",
      "    * Text\u5c5e\u6027\u548c\u5e03\u5c40\n",
      "    * \u5199\u6570\u5b66\u8868\u8fbe\u5f0f\n",
      "    * \u7528LaTeX\u6e32\u67d3\u6587\u672c\n",
      "    * \u6587\u672c\u6ce8\u91ca\n",
      "* [Artist\u6559\u7a0b](http://matplotlib.org/users/artists.html)\n",
      "    * \u4ecb\u7ecd\n",
      "    * \u81ea\u5b9a\u4e49\u4f60\u7684\u5bf9\u8c61\n",
      "    * \u5bf9\u8c61\u5bb9\u5668\n",
      "    * Figure\u5bb9\u5668\n",
      "    * Axes\u5bb9\u5668\n",
      "    * Axis\u5bb9\u5668\n",
      "    * \u523b\u5ea6\u5bb9\u5668\n",
      "* [Path\u6559\u7a0b](http://matplotlib.sourceforge.net/users/path_tutorial.html)\n",
      "    * \u4ecb\u7ecd\n",
      "    * B\u00e9zier\u4f8b\u5b50\n",
      "    * \u590d\u5408\u8def\u5f84\n",
      "* [\u8f6c\u6362\u6559\u7a0b](http://matplotlib.sourceforge.net/users/transforms_tutorial.html)\n",
      "    * \u4ecb\u7ecd\n",
      "    * \u6570\u636e\u5750\u6807\n",
      "    * Axes\u5750\u6807\n",
      "    * \u6df7\u5408\u8f6c\u6362\n",
      "    * \u7528offset\u8f6c\u6362\u6765\u7a7f\u4ef6\u4e00\u4e2a\u9634\u5f71\u6548\u679c\n",
      "    * pipline\u8f6c\u6362\n",
      "    \n",
      "### 1.4.5.2 Matplotlib\u6587\u6863\n",
      "\n",
      "* [\u7528\u6237\u624b\u518c](http://matplotlib.sourceforge.net/users/index.html)\n",
      "* [\u5e38\u89c1\u95ee\u9898](http://matplotlib.sourceforge.net/faq/index.html)\n",
      "    * \u5b89\u88c5\n",
      "    * \u4f7f\u7528\n",
      "    * \u5982\u4f55\u4f7f\u7528\n",
      "    * \u6545\u969c\u6392\u9664\n",
      "    * \u73af\u5883\u53d8\u91cf\n",
      "* [\u5c4f\u5e55\u622a\u56fe](http://matplotlib.sourceforge.net/users/screenshots.html)\n",
      "\n",
      "### 1.4.5.3 \u4ee3\u7801\u6587\u6863\n",
      "\n",
      "\u4ee3\u7801\u90fd\u6709\u5f88\u597d\u7684\u6587\u6863\uff0c\u4f60\u53ef\u4ee5\u5728Python\u4f1a\u8bdd\u4e2d\u7528\u7279\u5b9a\u547d\u4ee4\u5f88\u5feb\u7684\u8bbf\u95ee\uff1a"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import pylab as pl\n",
      "help(pl.plot)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Help on function plot in module matplotlib.pyplot:\n",
        "\n",
        "plot(*args, **kwargs)\n",
        "    Plot lines and/or markers to the\n",
        "    :class:`~matplotlib.axes.Axes`.  *args* is a variable length\n",
        "    argument, allowing for multiple *x*, *y* pairs with an\n",
        "    optional format string.  For example, each of the following is\n",
        "    legal::\n",
        "    \n",
        "        plot(x, y)        # plot x and y using default line style and color\n",
        "        plot(x, y, 'bo')  # plot x and y using blue circle markers\n",
        "        plot(y)           # plot y using x as index array 0..N-1\n",
        "        plot(y, 'r+')     # ditto, but with red plusses\n",
        "    \n",
        "    If *x* and/or *y* is 2-dimensional, then the corresponding columns\n",
        "    will be plotted.\n",
        "    \n",
        "    An arbitrary number of *x*, *y*, *fmt* groups can be\n",
        "    specified, as in::\n",
        "    \n",
        "        a.plot(x1, y1, 'g^', x2, y2, 'g-')\n",
        "    \n",
        "    Return value is a list of lines that were added.\n",
        "    \n",
        "    By default, each line is assigned a different color specified by a\n",
        "    'color cycle'.  To change this behavior, you can edit the\n",
        "    axes.color_cycle rcParam.\n",
        "    \n",
        "    The following format string characters are accepted to control\n",
        "    the line style or marker:\n",
        "    \n",
        "    ================    ===============================\n",
        "    character           description\n",
        "    ================    ===============================\n",
        "    ``'-'``             solid line style\n",
        "    ``'--'``            dashed line style\n",
        "    ``'-.'``            dash-dot line style\n",
        "    ``':'``             dotted line style\n",
        "    ``'.'``             point marker\n",
        "    ``','``             pixel marker\n",
        "    ``'o'``             circle marker\n",
        "    ``'v'``             triangle_down marker\n",
        "    ``'^'``             triangle_up marker\n",
        "    ``'<'``             triangle_left marker\n",
        "    ``'>'``             triangle_right marker\n",
        "    ``'1'``             tri_down marker\n",
        "    ``'2'``             tri_up marker\n",
        "    ``'3'``             tri_left marker\n",
        "    ``'4'``             tri_right marker\n",
        "    ``'s'``             square marker\n",
        "    ``'p'``             pentagon marker\n",
        "    ``'*'``             star marker\n",
        "    ``'h'``             hexagon1 marker\n",
        "    ``'H'``             hexagon2 marker\n",
        "    ``'+'``             plus marker\n",
        "    ``'x'``             x marker\n",
        "    ``'D'``             diamond marker\n",
        "    ``'d'``             thin_diamond marker\n",
        "    ``'|'``             vline marker\n",
        "    ``'_'``             hline marker\n",
        "    ================    ===============================\n",
        "    \n",
        "    \n",
        "    The following color abbreviations are supported:\n",
        "    \n",
        "    ==========  ========\n",
        "    character   color\n",
        "    ==========  ========\n",
        "    'b'         blue\n",
        "    'g'         green\n",
        "    'r'         red\n",
        "    'c'         cyan\n",
        "    'm'         magenta\n",
        "    'y'         yellow\n",
        "    'k'         black\n",
        "    'w'         white\n",
        "    ==========  ========\n",
        "    \n",
        "    In addition, you can specify colors in many weird and\n",
        "    wonderful ways, including full names (``'green'``), hex\n",
        "    strings (``'#008000'``), RGB or RGBA tuples (``(0,1,0,1)``) or\n",
        "    grayscale intensities as a string (``'0.8'``).  Of these, the\n",
        "    string specifications can be used in place of a ``fmt`` group,\n",
        "    but the tuple forms can be used only as ``kwargs``.\n",
        "    \n",
        "    Line styles and colors are combined in a single format string, as in\n",
        "    ``'bo'`` for blue circles.\n",
        "    \n",
        "    The *kwargs* can be used to set line properties (any property that has\n",
        "    a ``set_*`` method).  You can use this to set a line label (for auto\n",
        "    legends), linewidth, anitialising, marker face color, etc.  Here is an\n",
        "    example::\n",
        "    \n",
        "        plot([1,2,3], [1,2,3], 'go-', label='line 1', linewidth=2)\n",
        "        plot([1,2,3], [1,4,9], 'rs',  label='line 2')\n",
        "        axis([0, 4, 0, 10])\n",
        "        legend()\n",
        "    \n",
        "    If you make multiple lines with one plot command, the kwargs\n",
        "    apply to all those lines, e.g.::\n",
        "    \n",
        "        plot(x1, y1, x2, y2, antialised=False)\n",
        "    \n",
        "    Neither line will be antialiased.\n",
        "    \n",
        "    You do not need to use format strings, which are just\n",
        "    abbreviations.  All of the line properties can be controlled\n",
        "    by keyword arguments.  For example, you can set the color,\n",
        "    marker, linestyle, and markercolor with::\n",
        "    \n",
        "        plot(x, y, color='green', linestyle='dashed', marker='o',\n",
        "             markerfacecolor='blue', markersize=12).\n",
        "    \n",
        "    See :class:`~matplotlib.lines.Line2D` for details.\n",
        "    \n",
        "    The kwargs are :class:`~matplotlib.lines.Line2D` properties:\n",
        "    \n",
        "      agg_filter: unknown\n",
        "      alpha: float (0.0 transparent through 1.0 opaque)         \n",
        "      animated: [True | False]         \n",
        "      antialiased or aa: [True | False]         \n",
        "      axes: an :class:`~matplotlib.axes.Axes` instance         \n",
        "      clip_box: a :class:`matplotlib.transforms.Bbox` instance         \n",
        "      clip_on: [True | False]         \n",
        "      clip_path: [ (:class:`~matplotlib.path.Path`,         :class:`~matplotlib.transforms.Transform`) |         :class:`~matplotlib.patches.Patch` | None ]         \n",
        "      color or c: any matplotlib color         \n",
        "      contains: a callable function         \n",
        "      dash_capstyle: ['butt' | 'round' | 'projecting']         \n",
        "      dash_joinstyle: ['miter' | 'round' | 'bevel']         \n",
        "      dashes: sequence of on/off ink in points         \n",
        "      drawstyle: ['default' | 'steps' | 'steps-pre' | 'steps-mid' |                   'steps-post']         \n",
        "      figure: a :class:`matplotlib.figure.Figure` instance         \n",
        "      fillstyle: ['full' | 'left' | 'right' | 'bottom' | 'top' | 'none']         \n",
        "      gid: an id string         \n",
        "      label: string or anything printable with '%s' conversion.         \n",
        "      linestyle or ls: [``'-'`` | ``'--'`` | ``'-.'`` | ``':'`` | ``'None'`` |                   ``' '`` | ``''``]\n",
        "      linewidth or lw: float value in points         \n",
        "      lod: [True | False]         \n",
        "      marker: unknown\n",
        "      markeredgecolor or mec: any matplotlib color         \n",
        "      markeredgewidth or mew: float value in points         \n",
        "      markerfacecolor or mfc: any matplotlib color         \n",
        "      markerfacecoloralt or mfcalt: any matplotlib color         \n",
        "      markersize or ms: float         \n",
        "      markevery: unknown\n",
        "      path_effects: unknown\n",
        "      picker: float distance in points or callable pick function         ``fn(artist, event)``         \n",
        "      pickradius: float distance in points         \n",
        "      rasterized: [True | False | None]         \n",
        "      sketch_params: unknown\n",
        "      snap: unknown\n",
        "      solid_capstyle: ['butt' | 'round' |  'projecting']         \n",
        "      solid_joinstyle: ['miter' | 'round' | 'bevel']         \n",
        "      transform: a :class:`matplotlib.transforms.Transform` instance         \n",
        "      url: a url string         \n",
        "      visible: [True | False]         \n",
        "      xdata: 1D array         \n",
        "      ydata: 1D array         \n",
        "      zorder: any number         \n",
        "    \n",
        "    kwargs *scalex* and *scaley*, if defined, are passed on to\n",
        "    :meth:`~matplotlib.axes.Axes.autoscale_view` to determine\n",
        "    whether the *x* and *y* axes are autoscaled; the default is\n",
        "    *True*.\n",
        "    \n",
        "    \n",
        "    \n",
        "    Additional kwargs: hold = [True|False] overrides default hold state\n",
        "\n"
       ]
      }
     ],
     "prompt_number": 3
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "### 1.4.5.4 \u753b\u5eca\n",
      "\n",
      "\u5f53\u4f60\u641c\u7d22\u5982\u4f55\u63d0\u4f9b\u4e00\u4e2a\u7279\u5b9a\u56fe\u7247\u65f6\uff0c[matplotlib\u753b\u5eca](http://matplotlib.sourceforge.net/gallery.html)\u4e5f\u975e\u5e38\u6709\u7528\u3002\u6bcf\u4e2a\u4f8b\u5b50\u90fd\u6709\u6e90\u7801\u3002\n",
      "\n",
      "[\u8fd9\u91cc](http://www.loria.fr/~rougier/coding/gallery/)\u6709\u4e00\u4e2a\u5c0f\u7684\u753b\u5eca\u3002\n",
      "\n",
      "### 1.4.5.5 \u90ae\u4ef6\u5217\u8868\n",
      "\n",
      "\u6700\u540e\uff0c\u4f60\u53ef\u4ee5\u5728[\u7528\u6237\u90ae\u4ef6\u5217\u8868](https://lists.sourceforge.net/lists/listinfo/matplotlib-users)\u5bfb\u6c42\u5e2e\u52a9\uff0c\u800c[\u5f00\u53d1\u8005\u90ae\u4ef6\u5217\u8868](https://lists.sourceforge.net/lists/listinfo/matplotlib-devel)\u5219\u66f4\u504f\u6280\u672f\u3002\n",
      "\n",
      "## 1.4.6 \u5feb\u901f\u53c2\u8003\n",
      "\n",
      "\u8fd9\u91cc\u662f\u4e00\u7ec4\u8868\u683c\uff0c\u663e\u793a\u4e86\u4e3b\u8981\u7684\u5c5e\u6027\u548c\u6837\u5f0f\u3002\n",
      "\n",
      "### 1.4.6.1 Line\u5c5e\u6027\n",
      "\n",
      "<table border=\"1\" class=\"docutils\">\n",
      "<colgroup>\n",
      "<col width=\"20%\">\n",
      "<col width=\"30%\">\n",
      "<col width=\"50%\">\n",
      "</colgroup>\n",
      "<thead valign=\"bottom\">\n",
      "<tr class=\"row-odd\"><th class=\"head\">\u5c5e\u6027</th>\n",
      "<th class=\"head\">\u63cf\u8ff0</th>\n",
      "<th class=\"head\">\u5916\u89c2</th>\n",
      "</tr>\n",
      "</thead>\n",
      "<tbody valign=\"top\">\n",
      "<tr class=\"row-even\"><td>alpha (or a)</td>\n",
      "<td>alpha 0-1\u8303\u56f4\u7684\u900f\u660e\u5ea6</td>\n",
      "<td><img alt=\"plot_alpha_1.png\" class=\"first last\" src=\"http://scipy-lectures.github.io/_images/plot_alpha_1.png\">\n",
      "</td>\n",
      "</tr>\n",
      "<tr class=\"row-odd\"><td>antialiased</td>\n",
      "<td>True or False - use antialised rendering</td>\n",
      "<td><img alt=\"plot_aliased_1.png\" class=\"first\" src=\"http://scipy-lectures.github.io/_images/plot_aliased_1.png\">\n",
      "<img alt=\"plot_antialiased_1.png\" class=\"last\" src=\"http://scipy-lectures.github.io/_images/plot_antialiased_1.png\">\n",
      "</td>\n",
      "</tr>\n",
      "<tr class=\"row-even\"><td>color (or c)</td>\n",
      "<td>matplotlib\u989c\u8272\u53c2\u6570</td>\n",
      "<td><img alt=\"plot_color_1.png\" class=\"first last\" src=\"http://scipy-lectures.github.io/_images/plot_color_1.png\">\n",
      "</td>\n",
      "</tr>\n",
      "<tr class=\"row-odd\"><td>linestyle (or ls)</td>\n",
      "<td>see <a class=\"reference internal\" href=\"#line-properties\">Line\u5c5e\u6027</a></td>\n",
      "<td>&nbsp;</td>\n",
      "</tr>\n",
      "<tr class=\"row-even\"><td>linewidth (or lw)</td>\n",
      "<td>\u6d6e\u70b9, \u7ebf\u5bbd\u5ea6\u7528\u5c0f\u6570\u8868\u793a</td>\n",
      "<td><img alt=\"plot_linewidth_1.png\" class=\"first last\" src=\"http://scipy-lectures.github.io/_images/plot_linewidth_1.png\">\n",
      "</td>\n",
      "</tr>\n",
      "<tr class=\"row-odd\"><td>solid_capstyle</td>\n",
      "<td>\u5b9e\u7ebf\u5934\u7684\u6837\u5f0f</td>\n",
      "<td><img alt=\"plot_solid_capstyle_1.png\" class=\"first last\" src=\"http://scipy-lectures.github.io/_images/plot_solid_capstyle_1.png\">\n",
      "</td>\n",
      "</tr>\n",
      "<tr class=\"row-even\"><td>solid_joinstyle</td>\n",
      "<td>\u5b9e\u7ebf\u7684\u8fde\u63a5\u6837\u5f0f</td>\n",
      "<td><img alt=\"plot_solid_joinstyle_1.png\" class=\"first last\" src=\"http://scipy-lectures.github.io/_images/plot_solid_joinstyle_1.png\">\n",
      "</td>\n",
      "</tr>\n",
      "<tr class=\"row-odd\"><td>dash_capstyle</td>\n",
      "<td>\u865a\u7ebf\u5934\u7684\u6837\u5f0f</td>\n",
      "<td><img alt=\"plot_dash_capstyle_1.png\" class=\"first last\" src=\"http://scipy-lectures.github.io/_images/plot_dash_capstyle_1.png\">\n",
      "</td>\n",
      "</tr>\n",
      "<tr class=\"row-even\"><td>dash_joinstyle</td>\n",
      "<td>\u865a\u7ebf\u7684\u8fde\u63a5\u6837\u5f0f</td>\n",
      "<td><img alt=\"plot_dash_joinstyle_1.png\" class=\"first last\" src=\"http://scipy-lectures.github.io/_images/plot_dash_joinstyle_1.png\">\n",
      "</td>\n",
      "</tr>\n",
      "<tr class=\"row-odd\"><td>marker</td>\n",
      "<td>see <a class=\"reference internal\" href=\"#markers\">\u6807\u8bb0</a></td>\n",
      "<td>&nbsp;</td>\n",
      "</tr>\n",
      "<tr class=\"row-even\"><td>markeredgewidth (mew)</td>\n",
      "<td>\u6807\u8bb0\u7b26\u53f7\u7684\u7ebf\u5bbd\u5ea6</td>\n",
      "<td><img alt=\"plot_mew_1.png\" class=\"first last\" src=\"http://scipy-lectures.github.io/_images/plot_mew_1.png\">\n",
      "</td>\n",
      "</tr>\n",
      "<tr class=\"row-odd\"><td>markeredgecolor (mec)</td>\n",
      "<td>\u6807\u8bb0\u8fb9\u7f18\u7684\u989c\u8272</td>\n",
      "<td><img alt=\"plot_mec_1.png\" class=\"first last\" src=\"http://scipy-lectures.github.io/_images/plot_mec_1.png\">\n",
      "</td>\n",
      "</tr>\n",
      "<tr class=\"row-even\"><td>markerfacecolor (mfc)</td>\n",
      "<td>\u6807\u8bb0\u7684\u586b\u5145\u989c\u8272</td>\n",
      "<td><img alt=\"plot_mfc_1.png\" class=\"first last\" src=\"http://scipy-lectures.github.io/_images/plot_mfc_1.png\">\n",
      "</td>\n",
      "</tr>\n",
      "<tr class=\"row-odd\"><td>markersize (ms)</td>\n",
      "<td>\u6807\u8bb0\u7684\u5927\u5c0f\uff0c\u4ee5\u5c0f\u6570\u8868\u793a</td>\n",
      "<td><img alt=\"plot_ms_1.png\" class=\"first last\" src=\"http://scipy-lectures.github.io/_images/plot_ms_1.png\">\n",
      "</td>\n",
      "</tr>\n",
      "</tbody>\n",
      "</table>\n",
      "\n",
      "### 1.4.6.2 \u7ebf\u6837\u5f0f\n",
      "![plot_linestyles_1.png](http://scipy-lectures.github.io/_images/plot_linestyles_1.png)\n",
      "\n",
      "### 1.4.6.3 \u6807\u8bb0\n",
      "![plot_markers_1.png](http://scipy-lectures.github.io/_images/plot_markers_1.png)\n",
      "\n",
      "### 1.4.6.4 Colormaps\n",
      "\n",
      "\u6240\u6709colormap\u90fd\u53ef\u4ee5\u901a\u8fc7\u6dfb\u52a0`_r`\u6765\u8fdb\u884c\u989c\u8272\u53cd\u8f6c\u3002\u4f8b\u5982`gray_r`\u662f`gray`\u7684\u8865\u8272\u3002\n",
      "\n",
      "\u5982\u679c\u4f60\u60f3\u8981\u66f4\u591a\u4e86\u89e3colormaps\uff0c\u68c0\u67e5\u4e00\u4e0b[matplotlib colormaps\u7684\u6587\u6863](https://gist.github.com/2719900)\n",
      "\n",
      "![plot_colormaps_1.png](http://scipy-lectures.github.io/_images/plot_colormaps_1.png)"
     ]
    }
   ],
   "metadata": {}
  }
 ]
}