Python3：在某些点上绘制不带颜色的曲面

Z = np.array([[ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [ 0, 26067, 26067, 26750, 26750, 0, 0, 26673, 26673, 0, 0, 24411, 24411, 0, 0, 45494, 45494, 0], [ 0, 26067, 26067, 26750, 26750, 0, 0, 26673, 26673, 0, 0, 24411, 24411, 0, 0, 45494, 45494, 0], [ 0, 26750, 26750, 0, 0, 21237, 21237, 25516, 25516, 25839, 25839, 0, 0, 0, 0, 41704, 41704, 0], [ 0, 26750, 26750, 0, 0, 21237, 21237, 25516, 25516, 25839, 25839, 0, 0, 0, 0, 41704, 41704, 0], [ 0, 0, 0, 21236, 21236, 26414, 26414, 0, 0, 22847, 22847, 0, 0, 27051, 27051, 0, 0, 0], [ 0, 0, 0, 21236, 21236, 26414, 26414, 0, 0, 22847, 22847, 0, 0, 27051, 27051, 0, 0, 0], [ 0, 26673, 26673, 25516, 25516, 0, 0, 26414, 26414, 0, 0, 0, 0, 45013, 45013, 0, 0, 0], [ 0, 26673, 26673, 25516, 25516, 0, 0, 26414, 26414, 0, 0, 0, 0, 45013, 45013, 0, 0, 0], [ 0, 0, 0, 25839, 25839, 22860, 22860, 0, 0, 26213, 26213, 39181, 39181, 0, 0, 0, 0, 0], [ 0, 0, 0, 25839, 25839, 22860, 22860, 0, 0, 26213, 26213, 39181, 39181, 0, 0, 0, 0, 0], [ 0, 24411, 24411, 0, 0, 0, 0, 0, 0, 39183, 39183, 0, 0, 0, 0, 0, 0, 0], [ 0, 24411, 24411, 0, 0, 0, 0, 0, 0, 39183, 39183, 0, 0, 0, 0, 0, 0, 0], [ 0, 0, 0, 0, 0, 27052, 27052, 45015, 45015, 0, 0, 0, 0, 0, 0, 0, 0, 0], [ 0, 0, 0, 0, 0, 27052, 27052, 45015, 45015, 0, 0, 0, 0, 0, 0, 0, 0, 0], [ 0, 45494, 45494, 41434, 41434, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [ 0, 45494, 45494, 41434, 41434, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]) X = np.array([[ 0.5, 0.5, 1.5, 1.5, 2.5, 2.5, 3.5, 3.5, 4.5, 4.5, 5.5, 5.5, 6.5, 6.5, 7.5, 7.5, 8.5, 8.5], [ 0.5, 0.5, 1.5, 1.5, 2.5, 2.5, 3.5, 3.5, 4.5, 4.5, 5.5, 5.5, 6.5, 6.5, 7.5, 7.5, 8.5, 8.5], [ 0.5, 0.5, 1.5, 1.5, 2.5, 2.5, 3.5, 3.5, 4.5, 4.5, 5.5, 5.5, 6.5, 6.5, 7.5, 7.5, 8.5, 8.5], [ 0.5, 0.5, 1.5, 1.5, 2.5, 2.5, 3.5, 3.5, 4.5, 4.5, 5.5, 5.5, 6.5, 6.5, 7.5, 7.5, 8.5, 8.5], [ 0.5, 0.5, 1.5, 1.5, 2.5, 2.5, 3.5, 3.5, 4.5, 4.5, 5.5, 5.5, 6.5, 6.5, 7.5, 7.5, 8.5, 8.5], [ 0.5, 0.5, 1.5, 1.5, 2.5, 2.5, 3.5, 3.5, 4.5, 4.5, 5.5, 5.5, 6.5, 6.5, 7.5, 7.5, 8.5, 8.5], [ 0.5, 0.5, 1.5, 1.5, 2.5, 2.5, 3.5, 3.5, 4.5, 4.5, 5.5, 5.5, 6.5, 6.5, 7.5, 7.5, 8.5, 8.5], [ 0.5, 0.5, 1.5, 1.5, 2.5, 2.5, 3.5, 3.5, 4.5, 4.5, 5.5, 5.5, 6.5, 6.5, 7.5, 7.5, 8.5, 8.5], [ 0.5, 0.5, 1.5, 1.5, 2.5, 2.5, 3.5, 3.5, 4.5, 4.5, 5.5, 5.5, 6.5, 6.5, 7.5, 7.5, 8.5, 8.5], [ 0.5, 0.5, 1.5, 1.5, 2.5, 2.5, 3.5, 3.5, 4.5, 4.5, 5.5, 5.5, 6.5, 6.5, 7.5, 7.5, 8.5, 8.5], [ 0.5, 0.5, 1.5, 1.5, 2.5, 2.5, 3.5, 3.5, 4.5, 4.5, 5.5, 5.5, 6.5, 6.5, 7.5, 7.5, 8.5, 8.5], [ 0.5, 0.5, 1.5, 1.5, 2.5, 2.5, 3.5, 3.5, 4.5, 4.5, 5.5, 5.5, 6.5, 6.5, 7.5, 7.5, 8.5, 8.5], [ 0.5, 0.5, 1.5, 1.5, 2.5, 2.5, 3.5, 3.5, 4.5, 4.5, 5.5, 5.5, 6.5, 6.5, 7.5, 7.5, 8.5, 8.5], [ 0.5, 0.5, 1.5, 1.5, 2.5, 2.5, 3.5, 3.5, 4.5, 4.5, 5.5, 5.5, 6.5, 6.5, 7.5, 7.5, 8.5, 8.5], [ 0.5, 0.5, 1.5, 1.5, 2.5, 2.5, 3.5, 3.5, 4.5, 4.5, 5.5, 5.5, 6.5, 6.5, 7.5, 7.5, 8.5, 8.5], [ 0.5, 0.5, 1.5, 1.5, 2.5, 2.5, 3.5, 3.5, 4.5, 4.5, 5.5, 5.5, 6.5, 6.5, 7.5, 7.5, 8.5, 8.5], [ 0.5, 0.5, 1.5, 1.5, 2.5, 2.5, 3.5, 3.5, 4.5, 4.5, 5.5, 5.5, 6.5, 6.5, 7.5, 7.5, 8.5, 8.5], [ 0.5, 0.5, 1.5, 1.5, 2.5, 2.5, 3.5, 3.5, 4.5, 4.5, 5.5, 5.5, 6.5, 6.5, 7.5, 7.5, 8.5, 8.5]]) Y = np.array([[ 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5], [ 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5], [ 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5], [ 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5], [ 2.5, 2.5, 2.5, 2.5, 2.5, 2.5, 2.5, 2.5, 2.5, 2.5, 2.5, 2.5, 2.5, 2.5, 2.5, 2.5, 2.5, 2.5], [ 2.5, 2.5, 2.5, 2.5, 2.5, 2.5, 2.5, 2.5, 2.5, 2.5, 2.5, 2.5, 2.5, 2.5, 2.5, 2.5, 2.5, 2.5], [ 3.5, 3.5, 3.5, 3.5, 3.5, 3.5, 3.5, 3.5, 3.5, 3.5, 3.5, 3.5, 3.5, 3.5, 3.5, 3.5, 3.5, 3.5], [ 3.5, 3.5, 3.5, 3.5, 3.5, 3.5, 3.5, 3.5, 3.5, 3.5, 3.5, 3.5, 3.5, 3.5, 3.5, 3.5, 3.5, 3.5], [ 4.5, 4.5, 4.5, 4.5, 4.5, 4.5, 4.5, 4.5, 4.5, 4.5, 4.5, 4.5, 4.5, 4.5, 4.5, 4.5, 4.5, 4.5], [ 4.5, 4.5, 4.5, 4.5, 4.5, 4.5, 4.5, 4.5, 4.5, 4.5, 4.5, 4.5, 4.5, 4.5, 4.5, 4.5, 4.5, 4.5], [ 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5], [ 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5], [ 6.5, 6.5, 6.5, 6.5, 6.5, 6.5, 6.5, 6.5, 6.5, 6.5, 6.5, 6.5, 6.5, 6.5, 6.5, 6.5, 6.5, 6.5], [ 6.5, 6.5, 6.5, 6.5, 6.5, 6.5, 6.5, 6.5, 6.5, 6.5, 6.5, 6.5, 6.5, 6.5, 6.5, 6.5, 6.5, 6.5], [ 7.5, 7.5, 7.5, 7.5, 7.5, 7.5, 7.5, 7.5, 7.5, 7.5, 7.5, 7.5, 7.5, 7.5, 7.5, 7.5, 7.5, 7.5], [ 7.5, 7.5, 7.5, 7.5, 7.5, 7.5, 7.5, 7.5, 7.5, 7.5, 7.5, 7.5, 7.5, 7.5, 7.5, 7.5, 7.5, 7.5], [ 8.5, 8.5, 8.5, 8.5, 8.5, 8.5, 8.5, 8.5, 8.5, 8.5, 8.5, 8.5, 8.5, 8.5, 8.5, 8.5, 8.5, 8.5], [ 8.5, 8.5, 8.5, 8.5, 8.5, 8.5, 8.5, 8.5, 8.5, 8.5, 8.5, 8.5, 8.5, 8.5, 8.5, 8.5, 8.5, 8.5]])

from mpl_toolkits.mplot3d import Axes3D from matplotlib import cm import matplotlib.pyplot as plt fig, ax = plt.subplots() p = ax.pcolor(X, Y, Z, cmap=cm.plasma) #inferno, plasma, jet, sismic... fig.colorbar(p) plt.show()

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1楼 · 发布于 2024-05-20 13:17:19

与使用NAN相比，您可以通过屏蔽希望忽略的单元来实现这一点。Matplotlib将看到屏蔽的值，而不会打印它们

numpy.ma

https://docs.scipy.org/doc/numpy-1.15.1/reference/maskedarray.html

您可以通过将布尔数组作为索引或单独屏蔽每个单元格来屏蔽数组；这是一个非常灵活的模块

X = np.ma.masked_where(X>0.5, X)

比如说

编辑：我知道这是2D中的行为，尽管我不确定3D。我不清楚OP的问题是什么

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