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2-matplotlib_tutorial.ipynb 549 kB

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  1. {
  2. "cells": [
  3. {
  4. "cell_type": "markdown",
  5. "metadata": {},
  6. "source": [
  7. "# matplotlib\n",
  8. "\n",
  9. "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",
  10. "\n",
  11. "![cover image](images/Matplotlib_gallery.png)"
  12. ]
  13. },
  14. {
  15. "cell_type": "markdown",
  16. "metadata": {},
  17. "source": [
  18. "## 1. pyplot\n",
  19. "`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)。 "
  20. ]
  21. },
  22. {
  23. "cell_type": "code",
  24. "execution_count": 2,
  25. "metadata": {
  26. "scrolled": true
  27. },
  28. "outputs": [
  29. {
  30. "data": {
  31. 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vhfr1oX9/uPRSzVckdSdO76NRwCDCKOZtyYYjkr8WLgyDz2bMgLPOCjOaNm6cdlSSb+Ik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\nERZ12QAAAABJRU5ErkJggg==\n",
  32. "text/plain": [
  33. "<matplotlib.figure.Figure at 0x7f76e70e19e8>"
  34. ]
  35. },
  36. "metadata": {},
  37. "output_type": "display_data"
  38. }
  39. ],
  40. "source": [
  41. "# 这一行配置matplotlib以显示嵌入在notebook中的图形,\n",
  42. "# 而不是为每个图打开一个新窗口。稍后会详细介绍。\n",
  43. "%matplotlib inline\n",
  44. "\n",
  45. "import matplotlib.pyplot as plt\n",
  46. "\n",
  47. "plt.plot([1,2,3,4],[1,2,3,4], 'x-b')\n",
  48. "plt.ylabel('some numbers')\n",
  49. "plt.xlabel('variable')\n",
  50. "plt.show()"
  51. ]
  52. },
  53. {
  54. "cell_type": "code",
  55. "execution_count": 3,
  56. "metadata": {
  57. "lines_to_next_cell": 2
  58. },
  59. "outputs": [
  60. {
  61. "data": {
  62. "text/plain": [
  63. "[<matplotlib.lines.Line2D at 0x7f76e4344748>]"
  64. ]
  65. },
  66. "execution_count": 3,
  67. "metadata": {},
  68. "output_type": "execute_result"
  69. },
  70. {
  71. "data": {
  72. "image/png": 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Sxp/fX8aEr1bRtmkDnr4hkzOO1WZaUv0U6CJR9PV3mxk7bQFrC4u4ZlAGY8/tQbMG2kxL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\n5eYxyr9RAAAAAElFTkSuQmCC\n",
  73. "text/plain": [
  74. "<matplotlib.figure.Figure at 0x7f76e43e27b8>"
  75. ]
  76. },
  77. "metadata": {},
  78. "output_type": "display_data"
  79. }
  80. ],
  81. "source": [
  82. "plt.plot([1, 2, 3, 4], [1, 4, 9, 16])"
  83. ]
  84. },
  85. {
  86. "cell_type": "markdown",
  87. "metadata": {},
  88. "source": [
  89. "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:"
  90. ]
  91. },
  92. {
  93. "cell_type": "code",
  94. "execution_count": 4,
  95. "metadata": {},
  96. "outputs": [
  97. {
  98. "data": {
  99. "image/png": 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nYNdd867ILHelOOIfAsyJiHfaboiIDyLiw+z5ZGA9Sb5fnfWMZ55JwzsbbphCf+ed867I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\nfjOzGuPgNzOrMf8fniQ2J7ONZcwAAAAASUVORK5CYII=\n",
  100. "text/plain": [
  101. "<matplotlib.figure.Figure at 0x7f76e43d9a20>"
  102. ]
  103. },
  104. "metadata": {},
  105. "output_type": "display_data"
  106. }
  107. ],
  108. "source": [
  109. "import matplotlib.pyplot as plt\n",
  110. "plt.plot([1,2,3,4], [1,4,9,16], 'r.-')\n",
  111. "plt.axis([0, 6, 0, 20])\n",
  112. "plt.show()"
  113. ]
  114. },
  115. {
  116. "cell_type": "code",
  117. "execution_count": 5,
  118. "metadata": {},
  119. "outputs": [
  120. {
  121. "data": {
  122. "image/png": 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  123. "text/plain": [
  124. "<matplotlib.figure.Figure at 0x7f76e4310470>"
  125. ]
  126. },
  127. "metadata": {},
  128. "output_type": "display_data"
  129. }
  130. ],
  131. "source": [
  132. "import numpy as np\n",
  133. "import matplotlib.pyplot as plt\n",
  134. "\n",
  135. "# 以200ms间隔均匀采样时间\n",
  136. "t = np.arange(0., 5., 0.2)\n",
  137. "\n",
  138. "# 红色的破折号,蓝色的正方形和绿色的三角形\n",
  139. "plt.plot(t, t, 'r--', \\\n",
  140. " t, t**2, 'bs', \\\n",
  141. " t, t**3, 'g^')\n",
  142. "plt.show()"
  143. ]
  144. },
  145. {
  146. "cell_type": "markdown",
  147. "metadata": {},
  148. "source": [
  149. "### [控制线条属性](https://matplotlib.org/users/pyplot_tutorial.html#controlling-line-properties)\n",
  150. "\n",
  151. "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"
  152. ]
  153. },
  154. {
  155. "cell_type": "markdown",
  156. "metadata": {},
  157. "source": [
  158. "### 1.1 处理多个图形和轴\n",
  159. "\n",
  160. "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。"
  161. ]
  162. },
  163. {
  164. "cell_type": "code",
  165. "execution_count": 6,
  166. "metadata": {},
  167. "outputs": [
  168. {
  169. "data": {
  170. "image/png": 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4pWPHjhQU+JYHyeFwOGocIlKY6L1BmoCepuYfWnE4HI6MITAFEGjCspISKExY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\nHA6HI0txCsDhcDiyFKcAHA6HI0v5/y/7sIYUr/zoAAAAAElFTkSuQmCC\n",
  171. "text/plain": [
  172. "<matplotlib.figure.Figure at 0x7f76e427e438>"
  173. ]
  174. },
  175. "metadata": {},
  176. "output_type": "display_data"
  177. }
  178. ],
  179. "source": [
  180. "import numpy as np\n",
  181. "import matplotlib.pyplot as plt\n",
  182. "\n",
  183. "def f(t):\n",
  184. " return np.exp(-t) * np.cos(2*np.pi*t)\n",
  185. "\n",
  186. "t1 = np.arange(0.0, 5.0, 0.1)\n",
  187. "t2 = np.arange(0.0, 5.0, 0.02)\n",
  188. "\n",
  189. "plt.figure(1)\n",
  190. "plt.subplot(2,1,1)\n",
  191. "plt.plot(t1, f(t1), 'bo', t2, f(t2), 'k')\n",
  192. "\n",
  193. "plt.subplot(2,1,2)\n",
  194. "plt.plot(t2, np.cos(2*np.pi*t2), 'r--')\n",
  195. "plt.show()"
  196. ]
  197. },
  198. {
  199. "cell_type": "markdown",
  200. "metadata": {},
  201. "source": [
  202. "## 2. Image "
  203. ]
  204. },
  205. {
  206. "cell_type": "code",
  207. "execution_count": 7,
  208. "metadata": {},
  209. "outputs": [
  210. {
  211. "data": {

机器学习越来越多应用到飞行器、机器人等领域,其目的是利用计算机实现类似人类的智能,从而实现装备的智能化与无人化。本课程旨在引导学生掌握机器学习的基本知识、典型方法与技术,通过具体的应用案例激发学生对该学科的兴趣,鼓励学生能够从人工智能的角度来分析、解决飞行器、机器人所面临的问题和挑战。本课程主要内容包括Python编程基础,机器学习模型,无监督学习、监督学习、深度学习基础知识与实现,并学习如何利用机器学习解决实际问题,从而全面提升自我的《综合能力》。