Pyplot 比例尺(Scales)

在不同的比例上创建图。这里显示了线性，对数，对称对数和对数标度。有关更多示例，请参阅库的“缩放”部分。

  1. import numpy as np
  2. import matplotlib.pyplot as plt
  4. from matplotlib.ticker import NullFormatter  # useful for `logit` scale
  6. # Fixing random state for reproducibility
  7. np.random.seed(19680801)
  9. # make up some data in the interval ]0, 1[
  10. y = np.random.normal(loc=0.5, scale=0.4, size=1000)
  11. y = y[(y > 0) & (y < 1)]
  12. y.sort()
  13. x = np.arange(len(y))
  15. # plot with various axes scales
  16. plt.figure(1)
  18. # linear
  19. plt.subplot(221)
  20. plt.plot(x, y)
  21. plt.yscale('linear')
  22. plt.title('linear')
  23. plt.grid(True)
  26. # log
  27. plt.subplot(222)
  28. plt.plot(x, y)
  29. plt.yscale('log')
  30. plt.title('log')
  31. plt.grid(True)
  34. # symmetric log
  35. plt.subplot(223)
  36. plt.plot(x, y - y.mean())
  37. plt.yscale('symlog', linthreshy=0.01)
  38. plt.title('symlog')
  39. plt.grid(True)
  41. # logit
  42. plt.subplot(224)
  43. plt.plot(x, y)
  44. plt.yscale('logit')
  45. plt.title('logit')
  46. plt.grid(True)
  47. # Format the minor tick labels of the y-axis into empty strings with
  48. # `NullFormatter`, to avoid cumbering the axis with too many labels.
  49. plt.gca().yaxis.set_minor_formatter(NullFormatter())
  50. # Adjust the subplot layout, because the logit one may take more space
  51. # than usual, due to y-tick labels like "1 - 10^{-3}"
  52. plt.subplots_adjust(top=0.92, bottom=0.08, left=0.10, right=0.95, hspace=0.25,
  53.                     wspace=0.35)
  55. plt.show()

Pyplot 比例尺示例

参考

此示例中显示了以下函数，方法，类和模块的使用：

  1. import matplotlib
  2. matplotlib.pyplot.subplot
  3. matplotlib.pyplot.subplots_adjust
  4. matplotlib.pyplot.gca
  5. matplotlib.pyplot.yscale
  6. matplotlib.ticker.NullFormatter
  7. matplotlib.axis.Axis.set_minor_formatter

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