1.4Matplotlib：绘图.ipynb

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    "## 1.4.1 简介\n",
    "[Matplotlib](http://matplotlib.org/) 可能是Python惟一一个最广泛使用的二维图包。它同时提供了从Python中可视化数据非常的快速方式以及多种格式的出版质量图片。我们将在交互模式下研究Matplotlib，包含大多数的常用案例。\n",
    "\n",
    "### 1.4.1.1 IPython和pylab模式\n",
    "\n",
    "[IPython](http://ipython.org/)是强化版交互Python shell，有许多有趣的功能，包括：输入输出的命名、访问shell命令改进错误排除等。它位于Python中的科学计算工作流的核心，要让它与Matplotlib的结合使用：\n",
    "\n",
    "用命令行参数 `-pylab`（`--pylab` 从IPython0.12开始）启动IPython，获得带有Matlab/Mathematica类似功能的交互Matplotlib session。\n",
    "\n",
    "### 1.4.1.2 pylab\n",
    "\n",
    "*pylab*提供了matplotlib面向对象的绘图库的程序接口。它的模型与Matlab™非常相近。因此，pylab中的绝大多数绘图命令Matlab™都有带有相似函数的类似实现。重要的命令会以交互例子来解释。\n",
    "\n",
    "## 1.4.2 简单绘图\n",
    "\n",
    "在这个部分，我们将在同一个图像中绘制cosine和sine函数。从默认设置开始，我们将不断丰富图片，让它看起来更漂亮。\n",
    "\n",
    "第一步获得sine和cosine函数的数据："
   ]
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   "source": [
    "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)"
   ]
  },
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    "`X`现在是Numpy数组，范围是`-π`到`+π`之间（包含）的256个值。C是cosine（256个值），而S是sine（256个值）\n",
    "\n",
    "要运行例子，你可以在IPython的交互session中输入这些命令：\n",
    "\n",
    "```\n",
    "ipython --pylab\n",
    "```\n",
    "\n",
    "这会将我们带到IPython提示符：\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",
    "你可以下载每个示例，然后用平常的Python运行，但是，你将没法动态的数据操作：\n",
    "\n",
    "```\n",
    "python exercice_1.py\n",
    "```\n",
    "\n",
    "通过点击对应的图片，你可以获得每一步的源码。\n",
    "\n",
    "### 1.4.2.1 用默认设置绘图\n",
    "\n",
    "---\n",
    "**提示**：文档\n",
    "\n",
    "* [plot教程](http://matplotlib.sourceforge.net/users/pyplot_tutorial.html)\n",
    "* [plot()命令](http://matplotlib.sourceforge.net/api/pyplot_api.html#matplotlib.pyplot.plot)\n",
    "\n",
    "---\n",
    "\n",
    "Matplotlib有一组默认设置，允许自定义所有的属性。你几乎可以控制在matplotlib中的所有属性：图片大小和dpi、线长度、颜色和样式、坐标轴、坐标轴和网格属性、文本和字体属性等等。\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>"
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    "### 1.4.2.2 默认值示例\n",
    "\n",
    "---\n",
    "**提示**：文档\n",
    "\n",
    "* [自定义matplotlib](http://matplotlib.sourceforge.net/users/customizing.html)\n",
    "\n",
    "---\n",
    "\n",
    "在下面的脚本中，我们标示（备注）了影响绘图外观的所有图片设置。\n",
    "\n",
    "这些设置被显式的设置为默认值，但是现在你可以交互的实验这些值以便验证他们的效果（看一下下面的[线属性](http://scipy-lectures.github.io/intro/matplotlib/matplotlib.html#line-properties)和[线样式](http://scipy-lectures.github.io/intro/matplotlib/matplotlib.html#line-styles))。\n",
    "\n",
    "```python\n",
    "import pylab as pl\n",
    "import numpy as np\n",
    "\n",
    "# 创建一个大小为 8X6 英寸，每英寸80个点的图片\n",
    "pl.figure(figsize=(8, 6), dpi=80)\n",
    "\n",
    "# 从1X1的网格创建一个子图片\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",
    "# 用宽度为1（像素）的蓝色连续直线绘制cosine\n",
    "pl.plot(X, C, color=\"blue\", linewidth=1.0, linestyle=\"-\")\n",
    "\n",
    "# 用宽度为1（像素）的绿色连续直线绘制sine\n",
    "pl.plot(X, S, color=\"green\", linewidth=1.0, linestyle=\"-\")\n",
    "\n",
    "# 设置x轴的极值\n",
    "pl.xlim(-4.0, 4.0)\n",
    "\n",
    "# 设置x轴的刻度值\n",
    "pl.xticks(np.linspace(-4, 4, 9, endpoint=True))\n",
    "\n",
    "# 设置y轴的极值\n",
    "pl.ylim(-1.0, 1.0)\n",
    "\n",
    "# 设置y轴的刻度值\n",
    "pl.yticks(np.linspace(-1, 1, 5, endpoint=True))\n",
    "\n",
    "# 用72dpi保存图片\n",
    "# savefig(\"exercice_2.png\", dpi=72)\n",
    "\n",
    "# 在屏幕上显示结果\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 改变颜色和线宽度\n",
    "\n",
    "---\n",
    "**提示**：文档\n",
    "\n",
    "* [控制线属性](http://matplotlib.sourceforge.net/users/pyplot_tutorial.html#controlling-line-properties)\n",
    "* [线API](http://matplotlib.sourceforge.net/api/artist_api.html#matplotlib.lines.Line2D)\n",
    "\n",
    "---\n",
    "\n",
    "首先，我们想要cosine是蓝色，sine是红色，两者都是稍稍粗一点的线。我们也改变了一点图片的大小，让它更加水平。\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 设置极值\n",
    "\n",
    "---\n",
    "**提示**：文档\n",
    "\n",
    "- [xlim()命令](http://matplotlib.sourceforge.net/api/pyplot_api.html#matplotlib.pyplot.xlim)\n",
    "- [ylim()命令](http://matplotlib.sourceforge.net/api/pyplot_api.html#matplotlib.pyplot.ylim)\n",
    "\n",
    "---\n",
    "\n",
    "当前的图片的极值限制太拥挤了，我们希望留一点空间以便清晰的看到所有的数据点。\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 设置坐标轴刻度值\n",
    "\n",
    "**提示**：文档\n",
    "* [xticks()命令](http://matplotlib.sourceforge.net/api/pyplot_api.html#matplotlib.pyplot.xticks)\n",
    "* [yticks()命令](http://matplotlib.sourceforge.net/api/pyplot_api.html#matplotlib.pyplot.yticks)\n",
    "* [刻度容器](http://matplotlib.sourceforge.net/users/artists.html#axis-container)\n",
    "* [刻度位置和格式](http://matplotlib.sourceforge.net/api/ticker_api.html)\n",
    "\n",
    "现在的刻度不太理想，因为他们没有显示对于sine和cosine有意义的值（+/-π,+/-π/2）。我们将改变这些刻度，让他们只显示这些值。\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 设置刻度标签\n",
    "\n",
    "**提示**：文档\n",
    "* [操作文本](http://matplotlib.sourceforge.net/users/index_text.html)\n",
    "* [xticks()命令](http://matplotlib.sourceforge.net/api/pyplot_api.html#matplotlib.pyplot.xticks)\n",
    "* [yticks()命令](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",
    "刻度现在放在了正确的位置，但是标签并不是显而易见。我们能想到3.14是π，但是最好让它更明确。\n",
    "\n",
    "当我们设置了刻度值，我们也可以在第二个参数中列出对应的标签。注意我们用latex以便更好的渲染标签。\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 移动脊柱\n",
    "\n",
    "**提示**：文档\n",
    "* [脊柱](http://matplotlib.sourceforge.net/api/spines_api.html#matplotlib.spines)\n",
    "* [坐标轴容器](http://matplotlib.sourceforge.net/users/artists.html#axis-container)\n",
    "* [转换教程](http://matplotlib.sourceforge.net/users/transforms_tutorial.html)\n",
    "\n",
    "脊柱是连接坐标轴刻度标记的线，记录了数据范围的边界。他们可以被放在任意的位置，到目前位置，他们被放在了坐标轴的四周。我们将改变他们，因为我们希望他们在中间。因为有四条（上下左右），我们通过设置颜色为None舍弃了顶部和右侧，并且我们将把底部和左侧的脊柱移动到数据空间坐标的零点。\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 添加图例\n",
    "\n",
    "**提示**：文档\n",
    "* [图例指南](http://matplotlib.sourceforge.net/users/legend_guide.html)\n",
    "* [legend()命令](http://matplotlib.sourceforge.net/api/pyplot_api.html#matplotlib.pyplot.legend)\n",
    "* [图例API](http://matplotlib.sourceforge.net/api/legend_api.html#matplotlib.legend.Legend)\n",
    "\n",
    "让我们在坐上角添加图例。这只需要在plot命里中添加关键词参数label（将被用于图例框）。\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 标注一些点\n",
    "\n",
    "**提示**：文档\n",
    "* [注释坐标轴](http://matplotlib.sourceforge.net/users/annotations_guide.html)\n",
    "* [annotate()命令](http://matplotlib.sourceforge.net/api/pyplot_api.html#matplotlib.pyplot.annotate)\n",
    "\n",
    "让我们用annotate命令标注一些有趣的点。我们选取值2π/3，我们想要标注sine和cosine。首先我们在曲线上画出了一个垂直的散点标记线。然后，我们将用annotate命令显示带有剪头的文字。\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 细节是魔鬼\n",
    "\n",
    "**提示**：文档\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",
    "因为蓝色和红色的线，刻度标签很难看到。我们可以让他们更大一些，也可以调整他们的属性以便他们被处理为半透明的白色背景。这样我们就可以同时看到数据和标签。\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 图形、子图、轴和刻度\n",
    "\n",
    "在matplotlib中“**图形**”是用户界面中的整个窗口。在这个图形中可以有“**子图**”。\n",
    "\n",
    "到目前为止，我们已经使用图形和创建数轴。这对于快速绘图是非常方便的。使用图形、子图和轴我们可以控制显示。尽管子图将图表放在标准的网格中，轴可以在图形中放在任意位置。根据你的目的不同，二者都非常有用。我们也在没有显式的调用图形和子图时使用了他们。当我们调用plot时，matplotlib调用`gca()`来获得当前的坐标轴，相应的调用`gcf()`获得当前的图形。如果没有当前图形，那么将调用`figure()`去创建一个，严格来说是创建一个`subplot(111)。让我们来详细看一下。\n",
    "\n",
    "### 1.4.3.1 图形\n",
    "\n",
    "图形是在GUI中的窗口，标题是\"Figure #\"。图形的标号从1开始，而不是常规的Python方式从0开始。这明显是MATLAB-风格。这些参数决定图形的外观：\n",
    "\n",
    "参数|默认值|描述\n",
    "--------|-------|-----------\n",
    "num     |1      |图形编号\n",
    "figsize |figure.figsize|以英寸表示的图形大小（宽、高）\n",
    "dpi     |figure.dpi|分辨率以每英寸点数表示\n",
    "facecolor|figure.facecolor|背景色\n",
    "edgecolor|figure.edgecolor|背景边缘色\n",
    "frameon|True|是否绘制框架\n",
    "\n",
    "默认值可以在资源文件中指明，并在绝大数时间使用。只有图形数经常被改变。\n",
    "\n",
    "与其他对象类似，你可以用setp或者set_something方法设置图形属性。\n",
    "\n",
    "当你使用GUI工作时，你可以点击右上的X关闭图形。但是，你可以通过调用close用程序关闭图形。根据参数关闭不同内容（1）当前图形（没有参数），（2）特定图形（用图形编号或图形实例做参数），（3）所有图形（\"all\"作为参数）。\n",
    "\n",
    "```python\n",
    "pl.close(1)     # Closes figure 1\n",
    "```\n",
    "\n",
    "### 1.4.3.2 子图\n",
    "\n",
    "用子图你可以将图片放置在标准方格中。你需要指定行列数和图片数。 注意[gridspec](http://matplotlib.org/users/gridspec.html)命令相对更加高级。\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 轴\n",
    "\n",
    "轴与子图非常类似，不过允许图形放在图片的任意位置。因此，如果我们想要将一个小图形放在一个更大图形中，我们可以用轴。\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 刻度\n",
    "\n",
    "格式良好的刻度是准备好发布图片的必要部分。Matplotlib提供了一个完全可控的刻度系统。有刻度位置来指定刻度该出现在哪，还有刻度格式来给出你想要的刻度外观。主刻度和子刻度可以被独立放置和整理格式。之前子刻度默认是不显示的，即他们只有空列表，因为它是`NullLocator` (见下面)。\n",
    "\n",
    "#### 1.4.3.4.1 刻度位置\n",
    "\n",
    "刻度位置可以控制的位置。它的设置如下：\n",
    "\n",
    "```python\n",
    "ax = pl.gca()\n",
    "ax.xaxis.set_major_locator(eval(locator))\n",
    "```\n",
    "不同的需求有多种位置：\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",
    "所有这些位置都可以从基础类[matplotlib.ticker.Locator](http://matplotlib.org/api/ticker_api.html#matplotlib.ticker.Locator)衍生出来。你可以从中衍生出你自己的位置。将日期处理为刻度特别困哪。因此，`matplotlib`提供了特殊的位置`matplotlib.dates`。\n",
    "\n",
    "## 1.4.4 其他类型的图形：例子与练习\n",
    "\n",
    "### 1.4.4.1 常规图形\n",
    "\n",
    "---\n",
    "**提示：**你可以使用[fill_between](http://matplotlib.sourceforge.net/api/pyplot_api.html#matplotlib.pyplot.fill_between)命令。\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",
    "从下面的代码开始，试着重新生成这个图片，小心处理填充区域：\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",
    "点击图片查看答案。\n",
    "\n",
    "### 1.4.4.2 散点图\n",
    "\n",
    "---\n",
    "**提示：**颜色根据角度进行分配\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",
    "从下面的代码开始，试着重新生成这个图片，小心处理标记的大小颜色和透明度：\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",
    "点击图片查看答案。\n",
    "\n",
    "### 1.4.4.3 柱状图\n",
    "\n",
    "---\n",
    "**提示：**你需要小心文本对齐\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",
    "从下面的代码开始，试着重新生成这个图片，添加红柱的标签。\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",
    "点击图片查看答案。\n",
    "\n",
    "### 1.4.4.4 轮廓图\n",
    "\n",
    "---\n",
    "**提示：**你需要是使用[clabel](http://matplotlib.sourceforge.net/api/pyplot_api.html#matplotlib.pyplot.clabel)命令。\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",
    "从下面的代码开始，试着重新生成这个图片，小心处理colormap (见下面的[Colormaps](http://scipy-lectures.github.io/intro/matplotlib/matplotlib.html#colormaps))。\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",
    "点击图片查看答案。\n",
    "\n",
    "### 1.4.4.5 Imshow\n",
    "\n",
    "---\n",
    "**提示：**你需要小心处理在imshow命令中的图像*原点*并使用[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",
    "从下面的代码开始，试着重新生成这个图片，小心处理colormap和图像插入以及原点。\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",
    "点击图片查看答案。\n",
    "\n",
    "### 1.4.4.6 饼图\n",
    "\n",
    "---\n",
    "**提示：**你需要调整Z。\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",
    "从下面的代码开始，试着重新生成这个图片，小心处理颜色和切片大小。\n",
    "\n",
    "```python\n",
    "Z = np.random.uniform(0, 1, 20)\n",
    "pl.pie(Z)\n",
    "```\n",
    "点击图片查看答案。\n",
    "\n",
    "### 1.4.4.7 梯度图\n",
    "\n",
    "---\n",
    "**提示：**你需要绘制两次箭头。\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",
    "从下面的代码开始，试着重新生成这个图片，小心处理颜色和方向。\n",
    "\n",
    "```python\n",
    "n = 8\n",
    "X, Y = np.mgrid[0:n, 0:n]\n",
    "pl.quiver(X, Y)\n",
    "```\n",
    "点击图片查看答案。\n",
    "\n",
    "### 1.4.4.8 网格\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",
    "从下面的代码开始，试着重新生成这个图片，小心处理线的样式。\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",
    "点击图片查看答案。\n",
    "\n",
    "### 1.4.4.9 多图\n",
    "\n",
    "---\n",
    "**提示：**你可以用不同的分割来使用多个子图。\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",
    "点击图片查看答案。\n",
    "\n",
    "### 1.4.4.10 极坐标系\n",
    "\n",
    "---\n",
    "**提示：**你只需要修改`axes`行。\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",
    "从下面的代码开始，试着重新生成这个图片。\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",
    "点击图片查看答案。\n",
    "\n",
    "\n",
    "### 1.4.4.11 3D绘图\n",
    "\n",
    "---\n",
    "**提示：**你需要使用[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",
    "从下面的代码开始，试着重新生成这个图片。\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",
    "点击图片查看答案。\n",
    "\n",
    "**更多请见**：[用Mayavi 3D绘图](http://scipy-lectures.github.io/packages/3d_plotting/index.html#mayavi-label)\n",
    "\n",
    "### 1.4.4.12 文本\n",
    "\n",
    "---\n",
    "**提示：**看一下[matplotlib标识](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",
    "试着从0开始做这个事情！\n",
    "\n",
    "点击图片查看答案。\n",
    "\n",
    "---\n",
    "**快速阅读**\n",
    "\n",
    "如果你想要快速看一下Scipy讲座以便了解生态系统，你可以直接跳到下一章：Scipy：高级科学计算。\n",
    "\n",
    "本章的剩余部分对理解其他的介绍部分不是必须的。但是，请确保在稍后回来完成这个章节。\n",
    "\n",
    "## 1.4.5 这本教程之外\n",
    "\n",
    "Matplotlib从大量的文档以及用户和开发者社区中收益匪浅。这里是一些有趣的链接： \n",
    "\n",
    "### 1.4.5.1 教程\n",
    "\n",
    "* [Pyplot教程](http://matplotlib.sourceforge.net/users/pyplot_tutorial.html)\n",
    "    * 介绍\n",
    "    * 控制line属性\n",
    "    * 处理多个图形和坐标轴\n",
    "    * 处理文本\n",
    "* [Image教程](http://matplotlib.sourceforge.net/users/image_tutorial.html)\n",
    "    * 开始命令\n",
    "    * 从Numpy数组中导入图像数据\n",
    "    * 将numpy数组绘制为图像\n",
    "* [Text教程](http://matplotlib.sourceforge.net/users/index_text.html)\n",
    "    * Text介绍\n",
    "    * 基本text命令\n",
    "    * Text属性和布局\n",
    "    * 写数学表达式\n",
    "    * 用LaTeX渲染文本\n",
    "    * 文本注释\n",
    "* [Artist教程](http://matplotlib.org/users/artists.html)\n",
    "    * 介绍\n",
    "    * 自定义你的对象\n",
    "    * 对象容器\n",
    "    * Figure容器\n",
    "    * Axes容器\n",
    "    * Axis容器\n",
    "    * 刻度容器\n",
    "* [Path教程](http://matplotlib.sourceforge.net/users/path_tutorial.html)\n",
    "    * 介绍\n",
    "    * Bézier例子\n",
    "    * 复合路径\n",
    "* [转换教程](http://matplotlib.sourceforge.net/users/transforms_tutorial.html)\n",
    "    * 介绍\n",
    "    * 数据坐标\n",
    "    * Axes坐标\n",
    "    * 混合转换\n",
    "    * 用offset转换来穿件一个阴影效果\n",
    "    * pipline转换\n",
    "    \n",
    "### 1.4.5.2 Matplotlib文档\n",
    "\n",
    "* [用户手册](http://matplotlib.sourceforge.net/users/index.html)\n",
    "* [常见问题](http://matplotlib.sourceforge.net/faq/index.html)\n",
    "    * 安装\n",
    "    * 使用\n",
    "    * 如何使用\n",
    "    * 故障排除\n",
    "    * 环境变量\n",
    "* [屏幕截图](http://matplotlib.sourceforge.net/users/screenshots.html)\n",
    "\n",
    "### 1.4.5.3 代码文档\n",
    "\n",
    "代码都有很好的文档，你可以在Python会话中用特定命令很快的访问："
   ]
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     "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"
     ]
    }
   ],
   "source": [
    "import pylab as pl\n",
    "help(pl.plot)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.4.5.4 画廊\n",
    "\n",
    "当你搜索如何提供一个特定图片时，[matplotlib画廊](http://matplotlib.sourceforge.net/gallery.html)也非常有用。每个例子都有源码。\n",
    "\n",
    "[这里](http://www.loria.fr/~rougier/coding/gallery/)有一个小的画廊。\n",
    "\n",
    "### 1.4.5.5 邮件列表\n",
    "\n",
    "最后，你可以在[用户邮件列表](https://lists.sourceforge.net/lists/listinfo/matplotlib-users)寻求帮助，而[开发者邮件列表](https://lists.sourceforge.net/lists/listinfo/matplotlib-devel)则更偏技术。\n",
    "\n",
    "## 1.4.6 快速参考\n",
    "\n",
    "这里是一组表格，显示了主要的属性和样式。\n",
    "\n",
    "### 1.4.6.1 Line属性\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\">属性</th>\n",
    "<th class=\"head\">描述</th>\n",
    "<th class=\"head\">外观</th>\n",
    "</tr>\n",
    "</thead>\n",
    "<tbody valign=\"top\">\n",
    "<tr class=\"row-even\"><td>alpha (or a)</td>\n",
    "<td>alpha 0-1范围的透明度</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颜色参数</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属性</a></td>\n",
    "<td>&nbsp;</td>\n",
    "</tr>\n",
    "<tr class=\"row-even\"><td>linewidth (or lw)</td>\n",
    "<td>浮点, 线宽度用小数表示</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>实线头的样式</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>实线的连接样式</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>虚线头的样式</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>虚线的连接样式</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\">标记</a></td>\n",
    "<td>&nbsp;</td>\n",
    "</tr>\n",
    "<tr class=\"row-even\"><td>markeredgewidth (mew)</td>\n",
    "<td>标记符号的线宽度</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>标记边缘的颜色</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>标记的填充颜色</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>标记的大小，以小数表示</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 线样式\n",
    "![plot_linestyles_1.png](http://scipy-lectures.github.io/_images/plot_linestyles_1.png)\n",
    "\n",
    "### 1.4.6.3 标记\n",
    "![plot_markers_1.png](http://scipy-lectures.github.io/_images/plot_markers_1.png)\n",
    "\n",
    "### 1.4.6.4 Colormaps\n",
    "\n",
    "所有colormap都可以通过添加`_r`来进行颜色反转。例如`gray_r`是`gray`的补色。\n",
    "\n",
    "如果你想要更多了解colormaps，检查一下[matplotlib colormaps的文档](https://gist.github.com/2719900)\n",
    "\n",
    "![plot_colormaps_1.png](http://scipy-lectures.github.io/_images/plot_colormaps_1.png)"
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