title: Artifacts in a filled contour plot on 3D axes
body: <p>I have a frustrating problem that only manifests itself when plotting filled contour plots on 3D axes and only in certain situations.</p>
  
  <p>Here is an example of the issue I am experiencing:</p>
  
  <p><a href="https://i.stack.imgur.com/UnC1b.png" rel="noreferrer"><img src="https://i.stack.imgur.com/UnC1b.png" alt="enter image description here"></a></p>
  
  <p>and</p>
  
  <p><a href="https://i.stack.imgur.com/jhrqc.png" rel="noreferrer"><img src="https://i.stack.imgur.com/jhrqc.png" alt="enter image description here"></a></p>
  
  <p>These are the same data at different contouring intervals.
  You'll notice on the left side of the domain there is mis-filling occurring.  This is a plot with the Z points squished into the Z=0 plane, via a plotting command like</p>
  
  <pre><code>ax3d.contourf(X, Y, dbz[z25,:,:], zdir='z', offset=0, levels=levels, cmap='pymeteo_radar', alpha=0.50)
  </code></pre>
  
  <p>The miscontouring happens regardless of alpha level or colormap used, but is sensitive to the number of levels.  The use of <code>zdir</code> and <code>offset</code> do not effect the mis-contouring (the artifact just occurs on the Z surface.  If I do not fill the contour, there is no mis-contouring.  I can also alter the domain to sometimes make the issue better (or worse), but I have many plots to make within the same domain so that is not a fix.</p>
  
  <p>This issue does not occur when the same data is plotted on 2D axes, e.g.:</p>
  
  <p><a href="https://i.stack.imgur.com/tRfkI.jpg" rel="noreferrer"><img src="https://i.stack.imgur.com/tRfkI.jpg" alt="enter image description here"></a></p>
  
  <p>This plot has some extra data on it, but you can see that the filled contouring does not have the same artifact from mis-filling the contour that occurs on the 3d axes.</p>
  
  <p>Below is a script you can run to reproduce the issue.  </p>
  
  <pre><code>#!/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()
  </code></pre>
  
  <p>This code will produce the plot:</p>
  
  <p><a href="https://i.stack.imgur.com/y5vzB.png" rel="noreferrer"><img src="https://i.stack.imgur.com/y5vzB.png" alt="enter image description here"></a></p>
  
  <p>In all of the cases I have observed this mis-contouring the bad triangle always has a vertex near the bottom left of the domain.  My data is regularly gridded and for the domain in question is uniform in X and Y.  In this case the mis-filling will go away if the number of contour levels is reduced.  In some other cases this does not always help or just changes the visual appearance of the error.  In any case, even at very coarse contouring I still get errors in a subset of my plots.</p>
  
  <p>Has anyone seen this before and found a fix for it?  Am I overlooking something? I'm open to workarounds that don't involve lowering my contouring level (which does reduce the errors overall).  If others are in agreement that this could be a bug in the mplot3d, I will file a bug report with them (<a href="https://github.com/matplotlib/matplotlib/issues/4784" rel="noreferrer">Issue opened here</a>).  I have a feeling the problem lies with contouring very strong gradients when the <code>levels</code> option causes dense contours, but oddly only on 3d axes.</p>
  
  <p>Relevant version information:</p>
  
  <ul>
  <li>Python 3.4.1</li>
  <li>matplotlib 1.4.3</li>
  <li>numpy 1.9.0</li>
  </ul>