我有一个令人沮丧的问题，只有在 3D 轴上绘制填充轮廓图时才会出现，并且仅在某些情况下才会出现。
这是我遇到的问题的示例：
和
这些是不同等高线间隔的相同数据。您会注意到在域的左侧发生了错误填充。这是一个将 Z 点压入 Z=0 平面的图，通过类似的绘图命令
ax3d.contourf(X, Y, dbz[z25,:,:], zdir='z', offset=0, levels=levels, cmap='pymeteo_radar', alpha=0.50)
无论使用的 alpha 级别或颜色图如何，都会发生轮廓错误，但对级别的数量很敏感。使用zdir和offset不影响错误轮廓（伪影仅出现在 Z 表面上。如果我不填充轮廓，则没有错误轮廓。我还可以更改域以有时使问题更好（或更糟），但我在同一个域内有很多情节要制作，所以这不是解决办法。
当在 2D 轴上绘制相同的数据时，不会出现此问题，例如：
该图上有一些额外的数据，但您可以看到填充的轮廓与 3d 轴上发生的错误填充轮廓没有相同的伪影。
以下是您可以运行以重现该问题的脚本。
#!/usr/bin/env python
import numpy as np
import matplotlib.pyplot as plt
import mpl_toolkits.mplot3d.axes3d as p3
data=np.array([[53.9751, 51.5681, 50.7119, 51.1049, 51.5339, 51.4977, 51.2387,50.761, 50.1732, 49.8218, 49.5442, 48.936, 47.4498, 46.6484, 45.8542, 45.136, 44.5268, 44.071, 43.7665, 43.5928, 43.5269, 43.5385, 43.6053, 45.565, 47.0071, 46.8664, 47.372, 47.8324, 48.295, 48.731, 49.0522, 49.4001, 49.7111, 49.9919, 50.2527, 50.4928, 50.7135, 50.8831, 51.0806, 51.2683 ],
[55.6671, 52.53, 50.7764, 50.5632, 51.2095, 51.5659, 51.521, 51.2143, 50.653, 50.2371, 49.989, 49.8089, 49.6058, 47.8355, 47.3124, 46.7346, 46.1616, 45.6498, 45.2462, 44.967, 44.8005, 44.7284, 44.7295, 44.7869, 46.959, 45.0194, 46.73, 48.0766, 48.9395, 49.5325, 49.8498, 50.1887, 50.4798, 50.7406, 50.9808, 51.2003, 51.4074, 51.555, 51.7429, 51.9218 ],
[56.6513, 53.5919, 51.2774, 50.3133, 50.7705, 51.533, 51.8287, 51.7083, 51.2816, 50.7933, 50.4806, 50.2671, 50.1009, 50.0096, 49.9052, 49.4698, 47.4655, 47.0717, 46.6849, 46.3583, 46.1122, 45.952, 45.8678, 45.8485, 45.8811, 45.956, 46.0634, 47.2225, 49.4363, 50.2482, 50.527, 50.8558, 51.1358, 51.3809, 51.607, 51.8179, 52.0161, 52.1454, 52.3263, 52.497 ],
[57.078, 54.3224, 52.0759, 50.4679, 50.4677, 51.297, 52.0284, 52.1594, 51.9395, 51.5518, 51.1419, 50.8765, 50.6686, 50.5101, 50.4078, 50.3473, 50.3592, 50.3813, 49.7504, 47.55, 47.324, 47.1365, 46.9978, 46.9119, 46.8743, 46.8811, 46.9257, 47.0013, 50.0148, 50.9106, 51.1133, 51.4282, 51.7064, 51.943, 52.1587, 52.3597, 52.4789, 52.6631, 52.8359, 52.9966 ],
[57.3835, 54.9025, 52.8571, 50.9842, 50.5197, 51.1494, 52.0599, 52.4732, 52.4716, 52.2656, 51.9535, 51.6068, 51.3466, 51.1513, 50.9708, 50.8321, 50.7639, 50.7944, 50.8817, 49.8122, 48.2038, 48.086, 47.9704, 47.8735, 47.8035, 47.7644, 47.7574, 47.7803, 50.8194, 51.5486, 51.6645, 51.9745, 52.2349, 52.4508, 52.6481, 52.8317, 52.9412, 53.1097, 53.2699, 53.4171 ],
[57.9157, 55.6092, 53.6306, 51.8011, 50.9372, 51.2615, 52.1406, 52.7436, 52.8528, 52.7829, 52.6322, 52.403, 52.1149, 51.866, 51.6624, 51.4773, 51.317, 51.2183, 51.2153, 51.1367, 48.5913, 48.6216, 48.6218, 48.5951, 48.5589, 48.527, 48.5081, 50.5185, 51.6998, 51.905, 52.2258, 52.4891, 52.7062, 52.8926, 53.0655, 53.2251, 53.3262, 53.4755, 53.6169, 53.7471 ],
[58.6093, 56.432, 54.307, 52.6277, 51.584, 51.6482, 52.3762, 53.0685, 53.2545, 53.217, 53.1356, 53.0351, 52.8481, 52.6154, 52.39, 52.177, 51.9977, 51.843, 51.7172, 51.4587, 48.7481, 48.7984, 48.864, 48.9291, 48.9843, 49.0228, 50.496, 51.8667, 52.3404, 52.4759, 52.6889, 52.8851, 53.0525, 53.2072, 53.354, 53.4576, 53.5925, 53.7217, 53.8432, 53.956 ],
[58.9719, 56.9885, 54.8768, 53.3526, 52.3025, 52.2089, 52.7762, 53.4444, 53.6768, 53.6706, 53.5692, 53.5162, 53.4373, 53.2886, 53.1113, 52.9065, 52.6988, 52.5193, 52.3544, 52.0384, 48.9624, 48.9653, 49.0005, 49.0574, 49.1258, 50.692, 51.9726, 52.4309, 52.699, 52.8194, 52.9845, 53.1336, 53.2669, 53.393, 53.5118, 53.6086, 53.7213, 53.8293, 53.9308, 54.026 ],
[58.5754, 56.945, 55.068, 53.7798, 52.9469, 52.854, 53.3136,53.8929, 54.1205, 54.1178, 54.0128, 53.9289, 53.8906, 53.8239,53.717, 53.5724, 53.3818, 53.1892, 53.009, 49.3078, 49.2524,49.2165, 49.2032, 49.2187, 50.463, 51.9497, 52.4487, 52.7041,52.8358, 52.9776, 53.1101, 53.2293, 53.3419, 53.4487, 53.5401,53.6365, 53.7301, 53.8205, 53.9062, 53.9869 ],
[57.623, 56.547, 55.0117, 54.0512, 53.5372, 53.5246, 53.927,54.3868, 54.5828, 54.5811, 54.4501, 54.3235, 54.2626, 54.2334,54.1802, 54.1137, 53.9897, 53.8202, 49.796, 49.6864, 49.5946,49.5216, 49.4703, 49.4432, 51.8479, 52.5574, 52.8359, 52.9722,53.0827, 53.1826, 53.2747, 53.3597, 53.4405, 53.5138, 53.5944,53.6751, 53.7536, 53.829, 53.9019, 53.9721 ],
[56.902, 56.0005, 54.9159, 54.3352, 54.123, 54.2014, 54.5659,54.8917, 55.0307, 55.0139, 54.8838, 54.7044, 54.5863, 54.5548,54.5258, 54.4957, 54.4633, 51.4821, 50.1897, 50.0758, 49.9683,49.8704, 49.7842, 51.5064, 52.7625, 53.0724, 53.1926, 53.2682,53.3404, 53.4119, 53.4831, 53.5517, 53.6169, 53.6763, 53.7383,53.8009, 53.8644, 53.9281, 53.9905, 54.0517 ],
[56.3455, 55.5524, 54.9336, 54.6836, 54.703, 54.8657, 55.1749,55.3844, 55.4521, 55.4019, 55.2622, 55.0281, 54.8981, 54.6591,54.7866, 54.7678, 54.7654, 54.0436, 54.2302, 52.2533, 50.3305,50.2276, 50.1268, 52.9617, 53.4395, 53.5504, 53.5481, 53.5524,53.5699, 53.6014, 53.644, 53.6931, 53.7445, 53.7996, 53.8548,53.9097, 53.9655, 54.0229, 54.0813, 54.1393 ],
[55.7493, 55.3019, 55.1012, 55.0906, 55.234, 55.4751, 55.7134,55.8462, 55.8461, 55.7425, 55.5725, 55.3535, 55.1612, 54.958,55.0193, 54.9584, 54.9531, 54.8886, 54.8256, 54.2211, 50.6477,50.5564, 53.0546, 53.8592, 54.08, 54.0288, 53.9509, 53.8796,53.8307, 53.8073, 53.8034, 53.8142, 53.8383, 53.8725, 53.9128,53.9558, 54.0013, 54.0497, 54.103, 54.1597 ],
[55.2575, 55.1664, 55.3165, 55.5004, 55.7345, 55.9901, 56.1852,56.2599, 56.2027, 56.0454, 55.818, 55.5754, 55.302, 55.2083,55.0224, 55.1415, 55.0656, 55.0446, 55.0263, 54.7728, 50.8924,53.4671, 54.2587, 54.5146, 54.6171, 54.519, 54.3857, 54.2497,54.1355, 54.0509, 53.9932, 53.9584, 53.941, 53.939, 53.9527,53.9798, 54.0111, 54.0465, 54.0868, 54.1339 ],
[54.8665, 55.1533, 55.5095, 55.8512, 56.1541, 56.3995, 56.5593,56.6009, 56.5079, 56.3001, 56.0178, 55.7187, 55.448, 55.063,55.2016, 55.2116, 55.1817, 55.112, 55.1099, 55.0299, 54.3358,54.6966, 54.9199, 55.0156, 55.0728, 54.975, 54.8299, 54.6609,54.493, 54.3475, 54.2349, 54.1517, 54.0928, 54.0516, 54.0245,54.013, 54.0206, 54.0404, 54.0667, 54.0989 ],
[54.2676, 55.1132, 55.6112, 56.09, 56.428, 56.6661, 56.8056,56.8374, 56.7339, 56.4923, 56.1474, 55.7977, 55.4805, 55.2341,54.8999, 55.2662, 55.2927, 55.185, 55.1237, 55.1268, 54.9772,55.1418, 55.2612, 55.3333, 55.379, 55.3244, 55.2153, 55.0629,54.881, 54.6926, 54.523, 54.3866, 54.2855, 54.2118, 54.1583,54.1191, 54.0935, 54.0834, 54.0885, 54.1057 ],
[54.1771, 55.0795, 55.7075, 56.1772, 56.5183, 56.7522, 56.8898,56.9315, 56.8427, 56.6056, 56.2317, 55.8095, 55.4436, 55.183,55.0284, 54.9504, 55.2833, 55.2563, 55.1498, 55.1342, 55.1331,55.259, 55.3705, 55.4452, 55.4955, 55.5087, 55.4697, 55.3766,55.2324, 55.049, 54.8485, 54.6578, 54.4995, 54.3822, 54.3002,54.2427, 54.2022, 54.1749, 54.1598, 54.1561 ],
[53.9112, 54.85, 55.6641, 56.0844, 56.4062, 56.6232, 56.757,56.8149, 56.7669, 56.5754, 56.2311, 55.785, 55.366, 55.0104,54.812, 54.8845, 55.1273, 55.2339, 55.1976, 55.1049, 55.0913,55.1843, 55.3048, 55.4076, 55.4709, 55.518, 55.5455, 55.5329,55.4636, 55.3349, 55.1595, 54.9529, 54.7462, 54.5681, 54.4342,54.3439, 54.2848, 54.2446, 54.2222, 54.2135 ],
[53.9368, 54.9196, 55.4408, 55.7999, 56.0652, 56.2423, 56.348,56.4106, 56.4114, 56.3028, 56.0519, 55.6779, 55.2493, 54.8836,54.6592, 54.6347, 54.8341, 55.0606, 55.1396, 55.0967, 55.0325,55.0501, 55.1451, 55.2627, 55.3559, 55.4216, 55.4789, 55.5183,55.5245, 55.4779, 55.3701, 55.2072, 55.0029, 54.7876, 54.5915,54.4378, 54.3368, 54.2787, 54.2415, 54.2271 ],
[53.9325, 54.6506, 55.0421, 55.2926, 55.4603, 55.5679, 55.6285,55.6792, 55.7234, 55.731, 55.639, 55.3923, 55.043, 54.6845,54.4188, 54.3242, 54.4606, 54.7449, 54.9548, 55.0171, 55.0047,54.9454, 54.9666, 55.0651, 55.1828, 55.2677, 55.3308, 55.3914,55.438, 55.4544, 55.4277, 55.3385, 55.1907, 54.9981, 54.7786,54.5691, 54.4013, 54.2898, 54.233, 54.1994 ] ])
fig = plt.figure()
ax = fig.add_subplot(111,projection='3d')
X,Y = np.meshgrid(np.arange(-30.0,-20.0,0.25), np.arange(20.0,25,0.25))
ax.contourf(X,Y,data,zdir='z',offset=0, levels=np.arange(0,75,1))
ax.set_zlim(0.0,2.0)
plt.savefig('testfig.png')
plt.close()
此代码将生成图：
在所有情况下，我都观察到这种错误轮廓的坏三角形总是在域的左下角附近有一个顶点。我的数据是定期网格化的，并且所讨论的域在 X 和 Y 中是统一的。在这种情况下，如果轮廓级别的数量减少，错误填充就会消失。在其他一些情况下，这并不总是有帮助或只是改变错误的视觉外观。无论如何，即使在非常粗略的轮廓下，我仍然会在我的绘图子集中遇到错误。
有没有人以前见过这个并找到了解决方法？我忽略了什么吗？我愿意接受不涉及降低轮廓水平的解决方法（这确实减少了整体错误）。如果其他人同意这可能是 mplot3d 中的错误，我将向他们提交错误报告（在此处打开问题）。I have a feeling the problem lies with contouring very strong gradients when the levelsoption causes dense contours, but oddly only on 3d axes.
相关版本信息：
- Python 3.4.1
- matplotlib 1.4.3
- numpy 1.9.0
