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- {
- "cells": [
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "# matplotlib\n",
- "\n",
- "Matplotlib 是一个 Python 的 2D绘图库,它以各种硬拷贝格式和跨平台的交互式环境生成出版质量级别的图形。\n",
- "\n",
- ""
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## 1. pyplot\n",
- "`matplotlib.pyplot` 是一组命令风格的函数,它们使matplotlib的工作方式类似于MATLAB。每个pyplot函数都对图形进行一些更改:例如,创建图形,在图形中创建绘图区域,在绘图区域中绘制一些线,用标签装饰绘图,等等。在`matplotlib.pyplot`各种绘图状态都保存在函数调用过程,所以它跟踪当前图和绘图区域,和绘图功能是针对当前轴(请注意“axes”,在大多数地方的文档是指轴图的一部分,而不是严格的数学术语多个轴)。 "
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 1,
- "metadata": {
- "scrolled": true
- },
- "outputs": [
- {
- "data": {
- "image/png": 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- "text/plain": [
- "<matplotlib.figure.Figure at 0x7fd91846deb8>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "# 这一行配置matplotlib以显示嵌入在notebook中的图形,\n",
- "# 而不是为每个图打开一个新窗口。稍后会详细介绍。\n",
- "%matplotlib inline\n",
- "\n",
- "import matplotlib.pyplot as plt\n",
- "\n",
- "plt.plot([1,2,3,4],[1,2,3,4], 'x-b')\n",
- "plt.ylabel('some numbers')\n",
- "plt.xlabel('variable')\n",
- "plt.show()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 2,
- "metadata": {
- "lines_to_next_cell": 2
- },
- "outputs": [
- {
- "data": {
- "text/plain": [
- "[<matplotlib.lines.Line2D at 0x7fd9160e6550>]"
- ]
- },
- "execution_count": 2,
- "metadata": {},
- "output_type": "execute_result"
- },
- {
- "data": {
- "image/png": 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- "text/plain": [
- "<matplotlib.figure.Figure at 0x7fd9686dd710>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "plt.plot([1, 2, 3, 4], [1, 4, 9, 16])"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "对于每一对x、y参数,都有一个可选的第三个参数,它是表示图形的颜色和线条类型的格式字符串。格式字符串的字母和符号来自MATLAB,您可以将一个彩色字符串与一个行样式字符串连接起来。默认的格式字符串是' b- ',它是一条纯蓝色的线。例如,要用红色圆圈绘制上面的图形,您需要这样来设置:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 3,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "image/png": 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- "text/plain": [
- "<matplotlib.figure.Figure at 0x7fd916163f60>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "import matplotlib.pyplot as plt\n",
- "plt.plot([1,2,3,4], [1,4,9,16], 'r.-')\n",
- "plt.axis([0, 6, 0, 20])\n",
- "plt.show()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 4,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "image/png": 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- "text/plain": [
- "<matplotlib.figure.Figure at 0x7fd9160a7128>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "import numpy as np\n",
- "import matplotlib.pyplot as plt\n",
- "\n",
- "# 以200ms间隔均匀采样时间\n",
- "t = np.arange(0., 5., 0.2)\n",
- "\n",
- "# 红色的破折号,蓝色的正方形和绿色的三角形\n",
- "plt.plot(t, t, 'r--', \\\n",
- " t, t**2, 'bs', \\\n",
- " t, t**3, 'g^')\n",
- "plt.show()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### [控制线条属性](https://matplotlib.org/users/pyplot_tutorial.html#controlling-line-properties)\n",
- "\n",
- "线条有很多属性,你可以设置:线宽,折线样式,反锯齿,等等;看到matplotlib.lines.Line2D。设置行属性有几种方法\n"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### 1.1 处理多个图形和轴\n",
- "\n",
- "MATLAB和pyplot都有当前图和当前轴的概念。所有绘图命令都适用于当前轴。函数gca()返回当前轴(matplotlib.axes)。而gcf()返回当前的图(matplotlib.figure)。图实例)。通常,您不必担心这个,因为它都是在后台处理的。下面是创建两个次要情节的脚本。"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 5,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "image/png": 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YFi1asmlTFZtbxmKx+A5X8uGHmlpBRKpjDl2dBuQBf1HVqJc5LMrq1WsZNWoZ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\nrkJggg==\n",
- "text/plain": [
- "<matplotlib.figure.Figure at 0x7fd916016668>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "import numpy as np\n",
- "import matplotlib.pyplot as plt\n",
- "\n",
- "def f(t):\n",
- " return np.exp(-t) * np.cos(2*np.pi*t)\n",
- "\n",
- "t1 = np.arange(0.0, 5.0, 0.1)\n",
- "t2 = np.arange(0.0, 5.0, 0.02)\n",
- "\n",
- "plt.figure(1)\n",
- "plt.subplot(2,1,1)\n",
- "plt.plot(t1, f(t1), 'bo', t2, f(t2), 'k')\n",
- "\n",
- "plt.subplot(2,1,2)\n",
- "plt.plot(t2, np.cos(2*np.pi*t2), 'r--')\n",
- "plt.show()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## 2. Image "
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 6,
- "metadata": {},
- "outputs": [
- {
- "data": {
|