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2-matplotlib_tutorial.ipynb 492 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": 1,
  25. "metadata": {
  26. "scrolled": true
  27. },
  28. "outputs": [
  29. {
  30. "data": {
  31. "image/png": 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\n",
  32. "text/plain": [
  33. "<Figure size 432x288 with 1 Axes>"
  34. ]
  35. },
  36. "metadata": {
  37. "needs_background": "light"
  38. },
  39. "output_type": "display_data"
  40. }
  41. ],
  42. "source": [
  43. "# 这一行配置matplotlib以显示嵌入在notebook中的图形,\n",
  44. "# 而不是为每个图打开一个新窗口。稍后会详细介绍。\n",
  45. "%matplotlib inline\n",
  46. "\n",
  47. "import matplotlib.pyplot as plt\n",
  48. "\n",
  49. "plt.plot([1,2,3,4],[1,2,3,4], 'x-b')\n",
  50. "plt.ylabel('some numbers')\n",
  51. "plt.xlabel('variable')\n",
  52. "plt.show()"
  53. ]
  54. },
  55. {
  56. "cell_type": "code",
  57. "execution_count": 2,
  58. "metadata": {
  59. "lines_to_next_cell": 2
  60. },
  61. "outputs": [
  62. {
  63. "data": {
  64. "text/plain": [
  65. "[<matplotlib.lines.Line2D at 0x7f3780648810>]"
  66. ]
  67. },
  68. "execution_count": 2,
  69. "metadata": {},
  70. "output_type": "execute_result"
  71. },
  72. {
  73. "data": {
  74. "image/png": 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\n",
  75. "text/plain": [
  76. "<Figure size 432x288 with 1 Axes>"
  77. ]
  78. },
  79. "metadata": {
  80. "needs_background": "light"
  81. },
  82. "output_type": "display_data"
  83. }
  84. ],
  85. "source": [
  86. "plt.plot([1, 2, 3, 4], [1, 4, 9, 16])"
  87. ]
  88. },
  89. {
  90. "cell_type": "markdown",
  91. "metadata": {},
  92. "source": [
  93. "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:"
  94. ]
  95. },
  96. {
  97. "cell_type": "code",
  98. "execution_count": 3,
  99. "metadata": {},
  100. "outputs": [
  101. {
  102. "data": {
  103. "image/png": 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\n",
  104. "text/plain": [
  105. "<Figure size 432x288 with 1 Axes>"
  106. ]
  107. },
  108. "metadata": {
  109. "needs_background": "light"
  110. },
  111. "output_type": "display_data"
  112. }
  113. ],
  114. "source": [
  115. "import matplotlib.pyplot as plt\n",
  116. "plt.plot([1,2,3,4], [1,4,9,16], 'r.-')\n",
  117. "plt.axis([0, 6, 0, 20])\n",
  118. "plt.show()"
  119. ]
  120. },
  121. {
  122. "cell_type": "code",
  123. "execution_count": 4,
  124. "metadata": {},
  125. "outputs": [
  126. {
  127. "data": {
  128. "image/png": 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\n",
  129. "text/plain": [
  130. "<Figure size 432x288 with 1 Axes>"
  131. ]
  132. },
  133. "metadata": {
  134. "needs_background": "light"
  135. },
  136. "output_type": "display_data"
  137. }
  138. ],
  139. "source": [
  140. "import numpy as np\n",
  141. "import matplotlib.pyplot as plt\n",
  142. "\n",
  143. "# 以200ms间隔均匀采样时间\n",
  144. "t = np.arange(0., 5., 0.2)\n",
  145. "\n",
  146. "# 红色的破折号,蓝色的正方形和绿色的三角形\n",
  147. "plt.plot(t, t, 'r--', \\\n",
  148. " t, t**2, 'bs', \\\n",
  149. " t, t**3, 'g^')\n",
  150. "plt.show()"
  151. ]
  152. },
  153. {
  154. "cell_type": "markdown",
  155. "metadata": {},
  156. "source": [
  157. "### [控制线条属性](https://matplotlib.org/users/pyplot_tutorial.html#controlling-line-properties)\n",
  158. "\n",
  159. "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"
  160. ]
  161. },
  162. {
  163. "cell_type": "markdown",
  164. "metadata": {},
  165. "source": [
  166. "### 1.1 处理多个图形和轴\n",
  167. "\n",
  168. "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。"
  169. ]
  170. },
  171. {
  172. "cell_type": "code",
  173. "execution_count": 5,
  174. "metadata": {},
  175. "outputs": [
  176. {
  177. "data": {
  178. "image/png": 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\n",
  179. "text/plain": [
  180. "<Figure size 432x288 with 2 Axes>"
  181. ]
  182. },
  183. "metadata": {
  184. "needs_background": "light"
  185. },
  186. "output_type": "display_data"
  187. }
  188. ],
  189. "source": [
  190. "import numpy as np\n",
  191. "import matplotlib.pyplot as plt\n",
  192. "\n",
  193. "def f(t):\n",
  194. " return np.exp(-t) * np.cos(2*np.pi*t)\n",
  195. "\n",
  196. "t1 = np.arange(0.0, 5.0, 0.1)\n",
  197. "t2 = np.arange(0.0, 5.0, 0.02)\n",
  198. "\n",
  199. "plt.figure(1)\n",
  200. "plt.subplot(2,1,1)\n",
  201. "plt.plot(t1, f(t1), 'bo', t2, f(t2), 'k')\n",
  202. "\n",
  203. "plt.subplot(2,1,2)\n",
  204. "plt.plot(t2, np.cos(2*np.pi*t2), 'r--')\n",
  205. "plt.show()"
  206. ]
  207. },
  208. {
  209. "cell_type": "markdown",
  210. "metadata": {},
  211. "source": [
  212. "## 2. Image "
  213. ]
  214. },
  215. {
  216. "cell_type": "code",
  217. "execution_count": 6,
  218. "metadata": {},
  219. "outputs": [
  220. {
  221. "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》。