Tripcolor Demo

非结构化三角形网格的伪彩色图。

  1. import matplotlib.pyplot as plt
  2. import matplotlib.tri as tri
  3. import numpy as np

在不指定三角形的情况下创建三角剖分会导致点的Delaunay三角剖分。

  1. # First create the x and y coordinates of the points.
  2. n_angles = 36
  3. n_radii = 8
  4. min_radius = 0.25
  5. radii = np.linspace(min_radius, 0.95, n_radii)
  7. angles = np.linspace(0, 2 * np.pi, n_angles, endpoint=False)
  8. angles = np.repeat(angles[..., np.newaxis], n_radii, axis=1)
  9. angles[:, 1::2] += np.pi / n_angles
  11. x = (radii * np.cos(angles)).flatten()
  12. y = (radii * np.sin(angles)).flatten()
  13. z = (np.cos(radii) * np.cos(3 * angles)).flatten()
  15. # Create the Triangulation; no triangles so Delaunay triangulation created.
  16. triang = tri.Triangulation(x, y)
  18. # Mask off unwanted triangles.
  19. triang.set_mask(np.hypot(x[triang.triangles].mean(axis=1),
  20.                          y[triang.triangles].mean(axis=1))
  21.                 < min_radius)

tripcolor plot.

  1. fig1, ax1 = plt.subplots()
  2. ax1.set_aspect('equal')
  3. tpc = ax1.tripcolor(triang, z, shading='flat')
  4. fig1.colorbar(tpc)
  5. ax1.set_title('tripcolor of Delaunay triangulation, flat shading')

Tripcolor 演示

说明Gouraud阴影。

  1. fig2, ax2 = plt.subplots()
  2. ax2.set_aspect('equal')
  3. tpc = ax2.tripcolor(triang, z, shading='gouraud')
  4. fig2.colorbar(tpc)
  5. ax2.set_title('tripcolor of Delaunay triangulation, gouraud shading')

Tripcolor 演示2

您可以指定自己的三角剖分而不是执行点的Delaunay三角剖分，其中每个三角形由构成三角形的三个点的索引给出，以顺时针或逆时针方式排序。

  1. xy = np.asarray([
  2.     [-0.101, 0.872], [-0.080, 0.883], [-0.069, 0.888], [-0.054, 0.890],
  3.     [-0.045, 0.897], [-0.057, 0.895], [-0.073, 0.900], [-0.087, 0.898],
  4.     [-0.090, 0.904], [-0.069, 0.907], [-0.069, 0.921], [-0.080, 0.919],
  5.     [-0.073, 0.928], [-0.052, 0.930], [-0.048, 0.942], [-0.062, 0.949],
  6.     [-0.054, 0.958], [-0.069, 0.954], [-0.087, 0.952], [-0.087, 0.959],
  7.     [-0.080, 0.966], [-0.085, 0.973], [-0.087, 0.965], [-0.097, 0.965],
  8.     [-0.097, 0.975], [-0.092, 0.984], [-0.101, 0.980], [-0.108, 0.980],
  9.     [-0.104, 0.987], [-0.102, 0.993], [-0.115, 1.001], [-0.099, 0.996],
  10.     [-0.101, 1.007], [-0.090, 1.010], [-0.087, 1.021], [-0.069, 1.021],
  11.     [-0.052, 1.022], [-0.052, 1.017], [-0.069, 1.010], [-0.064, 1.005],
  12.     [-0.048, 1.005], [-0.031, 1.005], [-0.031, 0.996], [-0.040, 0.987],
  13.     [-0.045, 0.980], [-0.052, 0.975], [-0.040, 0.973], [-0.026, 0.968],
  14.     [-0.020, 0.954], [-0.006, 0.947], [ 0.003, 0.935], [ 0.006, 0.926],
  15.     [ 0.005, 0.921], [ 0.022, 0.923], [ 0.033, 0.912], [ 0.029, 0.905],
  16.     [ 0.017, 0.900], [ 0.012, 0.895], [ 0.027, 0.893], [ 0.019, 0.886],
  17.     [ 0.001, 0.883], [-0.012, 0.884], [-0.029, 0.883], [-0.038, 0.879],
  18.     [-0.057, 0.881], [-0.062, 0.876], [-0.078, 0.876], [-0.087, 0.872],
  19.     [-0.030, 0.907], [-0.007, 0.905], [-0.057, 0.916], [-0.025, 0.933],
  20.     [-0.077, 0.990], [-0.059, 0.993]])
  21. x, y = np.rad2deg(xy).T
  23. triangles = np.asarray([
  24.     [67, 66,  1], [65,  2, 66], [ 1, 66,  2], [64,  2, 65], [63,  3, 64],
  25.     [60, 59, 57], [ 2, 64,  3], [ 3, 63,  4], [ 0, 67,  1], [62,  4, 63],
  26.     [57, 59, 56], [59, 58, 56], [61, 60, 69], [57, 69, 60], [ 4, 62, 68],
  27.     [ 6,  5,  9], [61, 68, 62], [69, 68, 61], [ 9,  5, 70], [ 6,  8,  7],
  28.     [ 4, 70,  5], [ 8,  6,  9], [56, 69, 57], [69, 56, 52], [70, 10,  9],
  29.     [54, 53, 55], [56, 55, 53], [68, 70,  4], [52, 56, 53], [11, 10, 12],
  30.     [69, 71, 68], [68, 13, 70], [10, 70, 13], [51, 50, 52], [13, 68, 71],
  31.     [52, 71, 69], [12, 10, 13], [71, 52, 50], [71, 14, 13], [50, 49, 71],
  32.     [49, 48, 71], [14, 16, 15], [14, 71, 48], [17, 19, 18], [17, 20, 19],
  33.     [48, 16, 14], [48, 47, 16], [47, 46, 16], [16, 46, 45], [23, 22, 24],
  34.     [21, 24, 22], [17, 16, 45], [20, 17, 45], [21, 25, 24], [27, 26, 28],
  35.     [20, 72, 21], [25, 21, 72], [45, 72, 20], [25, 28, 26], [44, 73, 45],
  36.     [72, 45, 73], [28, 25, 29], [29, 25, 31], [43, 73, 44], [73, 43, 40],
  37.     [72, 73, 39], [72, 31, 25], [42, 40, 43], [31, 30, 29], [39, 73, 40],
  38.     [42, 41, 40], [72, 33, 31], [32, 31, 33], [39, 38, 72], [33, 72, 38],
  39.     [33, 38, 34], [37, 35, 38], [34, 38, 35], [35, 37, 36]])
  41. xmid = x[triangles].mean(axis=1)
  42. ymid = y[triangles].mean(axis=1)
  43. x0 = -5
  44. y0 = 52
  45. zfaces = np.exp(-0.01 * ((xmid - x0) * (xmid - x0) +
  46.                          (ymid - y0) * (ymid - y0)))

而不是创建Triangulation对象，可以直接将x，y和三角形数组传递给tripcolor。 如果要多次使用相同的三角测量来保存重复计算，最好使用Triangulation对象。 通过使用facecolors kwarg，可以为每个面指定一个颜色值，而不是每个点指定一个颜色值。

  1. fig3, ax3 = plt.subplots()
  2. ax3.set_aspect('equal')
  3. tpc = ax3.tripcolor(x, y, triangles, facecolors=zfaces, edgecolors='k')
  4. fig3.colorbar(tpc)
  5. ax3.set_title('tripcolor of user-specified triangulation')
  6. ax3.set_xlabel('Longitude (degrees)')
  7. ax3.set_ylabel('Latitude (degrees)')
  9. plt.show()

Tripcolor 演示3

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.axes.Axes.tripcolor
  3. matplotlib.pyplot.tripcolor
  4. matplotlib.tri
  5. matplotlib.tri.Triangulation

下载这个示例