不均匀分布图像

这说明了非统一图像类。它不是通过AXES方法提供的，但是可以很容易地将它添加到AXIS实例中，如下所示。

图像掩码示例

  1. import numpy as np
  2. import matplotlib.pyplot as plt
  3. from matplotlib.image import NonUniformImage
  4. from matplotlib import cm
  6. interp = 'nearest'
  8. # Linear x array for cell centers:
  9. x = np.linspace(-4, 4, 9)
  11. # Highly nonlinear x array:
  12. x2 = x**3
  14. y = np.linspace(-4, 4, 9)
  16. z = np.sqrt(x[np.newaxis, :]**2 + y[:, np.newaxis]**2)
  18. fig, axs = plt.subplots(nrows=2, ncols=2, constrained_layout=True)
  19. fig.suptitle('NonUniformImage class', fontsize='large')
  20. ax = axs[0, 0]
  21. im = NonUniformImage(ax, interpolation=interp, extent=(-4, 4, -4, 4),
  22.                      cmap=cm.Purples)
  23. im.set_data(x, y, z)
  24. ax.images.append(im)
  25. ax.set_xlim(-4, 4)
  26. ax.set_ylim(-4, 4)
  27. ax.set_title(interp)
  29. ax = axs[0, 1]
  30. im = NonUniformImage(ax, interpolation=interp, extent=(-64, 64, -4, 4),
  31.                      cmap=cm.Purples)
  32. im.set_data(x2, y, z)
  33. ax.images.append(im)
  34. ax.set_xlim(-64, 64)
  35. ax.set_ylim(-4, 4)
  36. ax.set_title(interp)
  38. interp = 'bilinear'
  40. ax = axs[1, 0]
  41. im = NonUniformImage(ax, interpolation=interp, extent=(-4, 4, -4, 4),
  42.                      cmap=cm.Purples)
  43. im.set_data(x, y, z)
  44. ax.images.append(im)
  45. ax.set_xlim(-4, 4)
  46. ax.set_ylim(-4, 4)
  47. ax.set_title(interp)
  49. ax = axs[1, 1]
  50. im = NonUniformImage(ax, interpolation=interp, extent=(-64, 64, -4, 4),
  51.                      cmap=cm.Purples)
  52. im.set_data(x2, y, z)
  53. ax.images.append(im)
  54. ax.set_xlim(-64, 64)
  55. ax.set_ylim(-4, 4)
  56. ax.set_title(interp)
  58. plt.show()

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