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matplotlib_simple_tutorial.ipynb 533 kB

7 years ago
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  1. {
  2. "cells": [
  3. {
  4. "cell_type": "markdown",
  5. "metadata": {},
  6. "source": [
  7. "# matplotlib\n",
  8. "\n"
  9. ]
  10. },
  11. {
  12. "cell_type": "markdown",
  13. "metadata": {},
  14. "source": [
  15. "## 1. pyplot\n",
  16. "matplotlib.pyplot is a collection of command style functions that make matplotlib work like MATLAB. Each pyplot function makes some change to a figure: e.g., creates a figure, creates a plotting area in a figure, plots some lines in a plotting area, decorates the plot with labels, etc. In matplotlib.pyplot various states are preserved across function calls, so that it keeps track of things like the current figure and plotting area, and the plotting functions are directed to the current axes (please note that “axes” here and in most places in the documentation refers to the axes part of a figure and not the strict mathematical term for more than one axis)."
  17. ]
  18. },
  19. {
  20. "cell_type": "code",
  21. "execution_count": 1,
  22. "metadata": {},
  23. "outputs": [
  24. {
  25. "data": {
  26. 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\n",
  27. "text/plain": [
  28. "<Figure size 432x288 with 1 Axes>"
  29. ]
  30. },
  31. "metadata": {},
  32. "output_type": "display_data"
  33. }
  34. ],
  35. "source": [
  36. "# This line configures matplotlib to show figures embedded in the notebook, \n",
  37. "# instead of opening a new window for each figure. More about that later. \n",
  38. "# If you are using an old version of IPython, try using '%pylab inline' instead.\n",
  39. "%matplotlib inline\n",
  40. "\n",
  41. "import matplotlib.pyplot as plt\n",
  42. "plt.plot([1,2,3,4])\n",
  43. "plt.ylabel('some numbers')\n",
  44. "plt.show()"
  45. ]
  46. },
  47. {
  48. "cell_type": "code",
  49. "execution_count": 2,
  50. "metadata": {},
  51. "outputs": [
  52. {
  53. "data": {
  54. "text/plain": [
  55. "[<matplotlib.lines.Line2D at 0x7fea26320400>]"
  56. ]
  57. },
  58. "execution_count": 2,
  59. "metadata": {},
  60. "output_type": "execute_result"
  61. },
  62. {
  63. "data": {
  64. "image/png": 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\n",
  65. "text/plain": [
  66. "<Figure size 432x288 with 1 Axes>"
  67. ]
  68. },
  69. "metadata": {},
  70. "output_type": "display_data"
  71. }
  72. ],
  73. "source": [
  74. "plt.plot([1, 2, 3, 4], [1, 4, 9, 16])\n"
  75. ]
  76. },
  77. {
  78. "cell_type": "markdown",
  79. "metadata": {},
  80. "source": [
  81. "For every x, y pair of arguments, there is an optional third argument which is the format string that indicates the color and line type of the plot. The letters and symbols of the format string are from MATLAB, and you concatenate a color string with a line style string. The default format string is ‘b-‘, which is a solid blue line. For example, to plot the above with red circles, you would issue"
  82. ]
  83. },
  84. {
  85. "cell_type": "code",
  86. "execution_count": 3,
  87. "metadata": {},
  88. "outputs": [
  89. {
  90. "data": {
  91. "image/png": 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\n",
  92. "text/plain": [
  93. "<Figure size 432x288 with 1 Axes>"
  94. ]
  95. },
  96. "metadata": {},
  97. "output_type": "display_data"
  98. }
  99. ],
  100. "source": [
  101. "import matplotlib.pyplot as plt\n",
  102. "plt.plot([1,2,3,4], [1,4,9,16], 'ro')\n",
  103. "plt.axis([0, 6, 0, 20])\n",
  104. "plt.show()"
  105. ]
  106. },
  107. {
  108. "cell_type": "code",
  109. "execution_count": 4,
  110. "metadata": {},
  111. "outputs": [
  112. {
  113. "data": {
  114. "image/png": 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\n",
  115. "text/plain": [
  116. "<Figure size 432x288 with 1 Axes>"
  117. ]
  118. },
  119. "metadata": {},
  120. "output_type": "display_data"
  121. }
  122. ],
  123. "source": [
  124. "import numpy as np\n",
  125. "import matplotlib.pyplot as plt\n",
  126. "\n",
  127. "# evenly sampled time at 200ms intervals\n",
  128. "t = np.arange(0., 5., 0.2)\n",
  129. "\n",
  130. "# red dashes, blue squares and green triangles\n",
  131. "plt.plot(t, t, 'r--', t, t**2, 'bs', t, t**3, 'g^')\n",
  132. "plt.show()"
  133. ]
  134. },
  135. {
  136. "cell_type": "markdown",
  137. "metadata": {},
  138. "source": [
  139. "### [Controlling line properties](https://matplotlib.org/users/pyplot_tutorial.html#controlling-line-properties)\n",
  140. "\n",
  141. "Lines have many attributes that you can set: linewidth, dash style, antialiased, etc; see matplotlib.lines.Line2D. There are several ways to set line properties\n"
  142. ]
  143. },
  144. {
  145. "cell_type": "markdown",
  146. "metadata": {},
  147. "source": [
  148. "### Working with multiple figures and axes\n",
  149. "\n",
  150. "MATLAB, and pyplot, have the concept of the current figure and the current axes. All plotting commands apply to the current axes. The function gca() returns the current axes (a matplotlib.axes.Axes instance), and gcf() returns the current figure (matplotlib.figure.Figure instance). Normally, you don’t have to worry about this, because it is all taken care of behind the scenes. Below is a script to create two subplots.\n",
  151. "\n"
  152. ]
  153. },
  154. {
  155. "cell_type": "code",
  156. "execution_count": 7,
  157. "metadata": {},
  158. "outputs": [
  159. {
  160. "data": {
  161. "image/png": 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\n",
  162. "text/plain": [
  163. "<Figure size 432x288 with 2 Axes>"
  164. ]
  165. },
  166. "metadata": {},
  167. "output_type": "display_data"
  168. }
  169. ],
  170. "source": [
  171. "import numpy as np\n",
  172. "import matplotlib.pyplot as plt\n",
  173. "\n",
  174. "def f(t):\n",
  175. " return np.exp(-t) * np.cos(2*np.pi*t)\n",
  176. "\n",
  177. "t1 = np.arange(0.0, 5.0, 0.1)\n",
  178. "t2 = np.arange(0.0, 5.0, 0.02)\n",
  179. "\n",
  180. "plt.figure(1)\n",
  181. "plt.subplot(211)\n",
  182. "plt.plot(t1, f(t1), 'bo', t2, f(t2), 'k')\n",
  183. "\n",
  184. "plt.subplot(212)\n",
  185. "plt.plot(t2, np.cos(2*np.pi*t2), 'r--')\n",
  186. "plt.show()"
  187. ]
  188. },
  189. {
  190. "cell_type": "markdown",
  191. "metadata": {},
  192. "source": [
  193. "## 2. Image "
  194. ]
  195. },
  196. {
  197. "cell_type": "code",
  198. "execution_count": 14,
  199. "metadata": {},
  200. "outputs": [
  201. {
  202. "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》。

Contributors (1)