如何简化和绘制这些数据？

Question

我有一些数据（三列多行），我想看看他们的发行版。如果我使用等高线图，结果很糟糕，

我想要的是下面的内容，

这是一个散点图，我可以看到有两条条纹比其余区域大。

我想知道绘制这些数字的好方法是什么？

数据如下，如果您愿意，请使用。

0.0 0.0 0.0
0.0 0.1 0.0213
0.0 0.2 0.08485
0.0 0.3 0.2913
0.0 0.4 0.3322
0.0 0.5 0.3391
0.0 0.6 1.062
0.0 0.7 1.08
0.0 0.8 1.161
0.0 0.9 1.549
0.0 1.0 1.121
0.0 1.1 1.901
0.0 1.2 3.332
0.0 1.3 1.627
0.0 1.4 1.995
0.0 1.5 2.893
0.0 1.6 3.128
0.0 1.7 5.083
0.0 1.8 3.918
0.0 1.9 3.688
0.0 2.0 2.614
0.0 2.1 4.866
0.0 2.2 2.798
0.0 2.3 2.889
0.0 2.4 6.914
0.0 2.5 3.217
0.0 2.6 5.306
0.0 2.7 7.475
0.0 2.8 3.828
0.0 2.9 7.855
0.0 3.0 5.093
0.1 0.0 0.003094
0.1 0.1 0.01241
0.1 0.2 0.1585
0.1 0.3 0.3186
0.1 0.4 0.5283
0.1 0.5 0.8057
0.1 0.6 1.088
0.1 0.7 1.435
0.1 0.8 1.77
0.1 0.9 2.111
0.1 1.0 2.508
0.1 1.1 2.849
0.1 1.2 3.231
0.1 1.3 3.5
0.1 1.4 3.818
0.1 1.5 4.144
0.1 1.6 4.388
0.1 1.7 4.53
0.1 1.8 4.934
0.1 1.9 5.255
0.1 2.0 5.002
0.1 2.1 5.172
0.1 2.2 5.576
0.1 2.3 5.719
0.1 2.4 5.966
0.1 2.5 6.306
0.1 2.6 6.584
0.1 2.7 6.76
0.1 2.8 7.029
0.1 2.9 7.264
0.1 3.0 7.71
0.2 0.0 0.01356
0.2 0.1 0.08406
0.2 0.2 0.2112
0.2 0.3 0.3966
0.2 0.4 0.6459
0.2 0.5 0.9651
0.2 0.6 1.358
0.2 0.7 1.76
0.2 0.8 2.161
0.2 0.9 2.672
0.2 1.0 3.079
0.2 1.1 3.428
0.2 1.2 3.64
0.2 1.3 3.94
0.2 1.4 4.244
0.2 1.5 4.494
0.2 1.6 4.766
0.2 1.7 4.972
0.2 1.8 5.323
0.2 1.9 5.737
0.2 2.0 4.956
0.2 2.1 5.216
0.2 2.2 5.751
0.2 2.3 6.214
0.2 2.4 6.909
0.2 2.5 7.34
0.2 2.6 7.833
0.2 2.7 8.647
0.2 2.8 9.162
0.2 2.9 9.713
0.2 3.0 10.26
0.3 0.0 0.03409
0.3 0.1 0.1219
0.3 0.2 0.2761
0.3 0.3 0.508
0.3 0.4 0.789
0.3 0.5 1.094
0.3 0.6 1.567
0.3 0.7 2.121
0.3 0.8 2.708
0.3 0.9 3.203
0.3 1.0 3.699
0.3 1.1 4.122
0.3 1.2 4.389
0.3 1.3 4.594
0.3 1.4 4.783
0.3 1.5 4.902
0.3 1.6 5.175
0.3 1.7 5.268
0.3 1.8 5.699
0.3 1.9 6.12
0.3 2.0 4.966
0.3 2.1 5.653
0.3 2.2 6.447
0.3 2.3 7.23
0.3 2.4 8.294
0.3 2.5 9.442
0.3 2.6 10.54
0.3 2.7 11.55
0.3 2.8 12.8
0.3 2.9 13.81
0.3 3.0 14.91
0.4 0.0 0.06764
0.4 0.1 0.1657
0.4 0.2 0.3447
0.4 0.3 0.6292
0.4 0.4 1.011
0.4 0.5 1.425
0.4 0.6 1.859
0.4 0.7 2.436
0.4 0.8 3.038
0.4 0.9 3.753
0.4 1.0 4.37
0.4 1.1 4.782
0.4 1.2 5.143
0.4 1.3 5.282
0.4 1.4 5.305
0.4 1.5 5.47
0.4 1.6 5.431
0.4 1.7 5.744
0.4 1.8 6.006
0.4 1.9 6.521
0.4 2.0 5.166
0.4 2.1 6.127
0.4 2.2 7.312
0.4 2.3 8.708
0.4 2.4 10.31
0.4 2.5 12.02
0.4 2.6 13.79
0.4 2.7 15.55
0.4 2.8 17.53
0.4 2.9 19.17
0.4 3.0 21.14
0.5 0.0 0.5008
0.5 0.1 0.2176
0.5 0.2 0.4307
0.5 0.3 0.7543
0.5 0.4 1.272
0.5 0.5 1.894
0.5 0.6 2.485
0.5 0.7 3.043
0.5 0.8 3.552
0.5 0.9 4.273
0.5 1.0 4.991
0.5 1.1 5.472
0.5 1.2 5.893
0.5 1.3 5.938
0.5 1.4 5.973
0.5 1.5 5.965
0.5 1.6 5.941
0.5 1.7 6.124
0.5 1.8 6.483
0.5 1.9 7.327
0.5 2.0 5.579
0.5 2.1 6.788
0.5 2.2 8.503
0.5 2.3 10.45
0.5 2.4 12.74
0.5 2.5 15.06
0.5 2.6 17.68
0.5 2.7 20.43
0.5 2.8 22.88
0.5 2.9 25.49
0.5 3.0 28.45
0.6 0.0 0.2062
0.6 0.1 0.2815
0.6 0.2 0.5172
0.6 0.3 0.9206
0.6 0.4 1.528
0.6 0.5 2.342
0.6 0.6 3.31
0.6 0.7 4.218
0.6 0.8 4.783
0.6 0.9 5.224
0.6 1.0 5.706
0.6 1.1 6.171
0.6 1.2 6.583
0.6 1.3 6.722
0.6 1.4 6.732
0.6 1.5 6.591
0.6 1.6 6.397
0.6 1.7 6.588
0.6 1.8 6.937
0.6 1.9 7.928
0.6 2.0 6.066
0.6 2.1 7.572
0.6 2.2 9.767
0.6 2.3 12.36
0.6 2.4 15.31
0.6 2.5 18.54
0.6 2.6 21.98
0.6 2.7 25.36
0.6 2.8 29.07
0.6 2.9 32.62
0.6 3.0 36.24
0.7 0.0 1.287
0.7 0.1 0.3431
0.7 0.2 0.6151
0.7 0.3 1.062
0.7 0.4 1.775
0.7 0.5 2.851
0.7 0.6 4.16
0.7 0.7 5.533
0.7 0.8 6.607
0.7 0.9 7.232
0.7 1.0 7.41
0.7 1.1 7.424
0.7 1.2 7.593
0.7 1.3 7.641
0.7 1.4 7.492
0.7 1.5 7.251
0.7 1.6 7.212
0.7 1.7 7.13
0.7 1.8 7.545
0.7 1.9 8.705
0.7 2.0 6.405
0.7 2.1 8.431
0.7 2.2 11.02
0.7 2.3 14.13
0.7 2.4 17.99
0.7 2.5 21.88
0.7 2.6 26.22
0.7 2.7 30.41
0.7 2.8 34.83
0.7 2.9 39.17
0.7 3.0 44.05
0.8 0.0 0.3007
0.8 0.1 0.4204
0.8 0.2 0.7097
0.8 0.3 1.227
0.8 0.4 2.048
0.8 0.5 3.287
0.8 0.6 4.926
0.8 0.7 6.736
0.8 0.8 8.413
0.8 0.9 9.618
0.8 1.0 10.03
0.8 1.1 9.887
0.8 1.2 9.466
0.8 1.3 8.925
0.8 1.4 8.549
0.8 1.5 8.099
0.8 1.6 7.929
0.8 1.7 7.8
0.8 1.8 8.159
0.8 1.9 9.361
0.8 2.0 6.804
0.8 2.1 9.117
0.8 2.2 12.1
0.8 2.3 15.78
0.8 2.4 20.19
0.8 2.5 24.69
0.8 2.6 30.16
0.8 2.7 35.57
0.8 2.8 40.9
0.8 2.9 46.33
0.8 3.0 51.89
0.9 0.0 1.354
0.9 0.1 0.4937
0.9 0.2 0.8159
0.9 0.3 1.374
0.9 0.4 2.327
0.9 0.5 3.752
0.9 0.6 5.712
0.9 0.7 7.885
0.9 0.8 10.08
0.9 0.9 11.77
0.9 1.0 12.82
0.9 1.1 12.8
0.9 1.2 12.24
0.9 1.3 11.12
0.9 1.4 10.1
0.9 1.5 9.312
0.9 1.6 8.807
0.9 1.7 8.479
0.9 1.8 8.714
0.9 1.9 5.51
0.9 2.0 7.008
0.9 2.1 9.543
0.9 2.2 12.85
0.9 2.3 17.03
0.9 2.4 21.71
0.9 2.5 27.29
0.9 2.6 33.7
0.9 2.7 39.52
0.9 2.8 46.49
0.9 2.9 52.68
0.9 3.0 58.89
1.0 0.0 1.231
1.0 0.1 0.5927
1.0 0.2 0.9244
1.0 0.3 1.532
1.0 0.4 2.601
1.0 0.5 4.225
1.0 0.6 6.332
1.0 0.7 8.884
1.0 0.8 11.46
1.0 0.9 13.73
1.0 1.0 15.19
1.0 1.1 15.71
1.0 1.2 15.0
1.0 1.3 13.95
1.0 1.4 12.14
1.0 1.5 10.75
1.0 1.6 9.605
1.0 1.7 9.143
1.0 1.8 9.166
1.0 1.9 5.484
1.0 2.0 7.032
1.0 2.1 9.596
1.0 2.2 13.34
1.0 2.3 17.78
1.0 2.4 23.43
1.0 2.5 29.16
1.0 2.6 36.1
1.0 2.7 43.09
1.0 2.8 50.13
1.0 2.9 57.59
1.0 3.0 65.69
1.1 0.0 1.192
1.1 0.1 0.6842
1.1 0.2 1.02
1.1 0.3 1.684
1.1 0.4 2.824
1.1 0.5 4.607
1.1 0.6 7.0
1.1 0.7 9.88
1.1 0.8 12.91
1.1 0.9 15.42
1.1 1.0 17.35
1.1 1.1 18.1
1.1 1.2 17.56
1.1 1.3 16.15
1.1 1.4 14.27
1.1 1.5 12.36
1.1 1.6 10.63
1.1 1.7 9.75
1.1 1.8 9.393
1.1 1.9 5.197
1.1 2.0 6.636
1.1 2.1 9.204
1.1 2.2 13.0
1.1 2.3 17.93
1.1 2.4 23.92
1.1 2.5 30.39
1.1 2.6 37.62
1.1 2.7 45.81
1.1 2.8 53.93
1.1 2.9 61.74
1.1 3.0 70.89
1.2 0.0 0.3645
1.2 0.1 0.7622
1.2 0.2 1.155
1.2 0.3 1.832
1.2 0.4 3.096
1.2 0.5 4.987
1.2 0.6 7.631
1.2 0.7 10.76
1.2 0.8 14.08
1.2 0.9 17.03
1.2 1.0 19.08
1.2 1.1 20.07
1.2 1.2 19.83
1.2 1.3 18.4
1.2 1.4 16.1
1.2 1.5 13.53
1.2 1.6 11.55
1.2 1.7 10.06
1.2 1.8 9.504
1.2 1.9 4.801
1.2 2.0 6.003
1.2 2.1 8.504
1.2 2.2 12.12
1.2 2.3 17.32
1.2 2.4 23.56
1.2 2.5 30.57
1.2 2.6 38.89
1.2 2.7 47.35
1.2 2.8 56.22
1.2 2.9 64.72
1.2 3.0 73.63
1.3 0.0 1.178
1.3 0.1 0.8407
1.3 0.2 1.238
1.3 0.3 1.967
1.3 0.4 3.3
1.3 0.5 5.354
1.3 0.6 8.239
1.3 0.7 11.64
1.3 0.8 15.29
1.3 0.9 18.36
1.3 1.0 20.49
1.3 1.1 21.64
1.3 1.2 21.4
1.3 1.3 19.57
1.3 1.4 17.61
1.3 1.5 14.44
1.3 1.6 12.08
1.3 1.7 10.27
1.3 1.8 9.436
1.3 1.9 9.954
1.3 2.0 11.2
1.3 2.1 13.85
1.3 2.2 17.53
1.3 2.3 23.02
1.3 2.4 29.89
1.3 2.5 37.33
1.3 2.6 46.32
1.3 2.7 55.57
1.3 2.8 65.95
1.3 2.9 75.27
1.3 3.0 85.77
1.4 0.0 1.164
1.4 0.1 0.472
1.4 0.2 0.4634
1.4 0.3 0.8173
1.4 0.4 1.799
1.4 0.5 3.584
1.4 0.6 6.143
1.4 0.7 9.492
1.4 0.8 12.82
1.4 0.9 16.11
1.4 1.0 18.29
1.4 1.1 19.42
1.4 1.2 18.82
1.4 1.3 16.79
1.4 1.4 14.09
1.4 1.5 10.64
1.4 1.6 7.546
1.4 1.7 5.273
1.4 1.8 3.995
1.4 1.9 4.094
1.4 2.0 5.189
1.4 2.1 7.504
1.4 2.2 10.99
1.4 2.3 15.64
1.4 2.4 21.67
1.4 2.5 29.09
1.4 2.6 37.84
1.4 2.7 47.77
1.4 2.8 57.7
1.4 2.9 69.47
1.4 3.0 78.44
1.5 0.0 0.4638
1.5 0.1 0.517
1.5 0.2 0.5069
1.5 0.3 0.8738
1.5 0.4 1.904
1.5 0.5 3.826
1.5 0.6 6.572
1.5 0.7 10.02
1.5 0.8 13.83
1.5 0.9 17.15
1.5 1.0 19.7
1.5 1.1 20.49
1.5 1.2 19.86
1.5 1.3 17.81
1.5 1.4 14.91
1.5 1.5 11.12
1.5 1.6 7.573
1.5 1.7 5.096
1.5 1.8 3.863
1.5 1.9 4.077
1.5 2.0 5.581
1.5 2.1 8.252
1.5 2.2 11.77
1.5 2.3 16.24
1.5 2.4 21.77
1.5 2.5 29.0
1.5 2.6 37.43
1.5 2.7 47.16
1.5 2.8 57.15
1.5 2.9 68.55
1.5 3.0 80.21
1.6 0.0 0.4721
1.6 0.1 0.5487
1.6 0.2 0.5378
1.6 0.3 0.9291
1.6 0.4 2.005
1.6 0.5 4.028
1.6 0.6 6.937
1.6 0.7 10.56
1.6 0.8 14.51
1.6 0.9 17.99
1.6 1.0 20.58
1.6 1.1 21.54
1.6 1.2 20.79
1.6 1.3 18.85
1.6 1.4 15.27
1.6 1.5 11.24
1.6 1.6 7.736
1.6 1.7 4.929
1.6 1.8 3.838
1.6 1.9 4.288
1.6 2.0 6.551
1.6 2.1 9.946
1.6 2.2 14.09
1.6 2.3 18.84
1.6 2.4 24.22
1.6 2.5 30.5
1.6 2.6 37.71
1.6 2.7 47.01
1.6 2.8 56.91
1.6 2.9 68.17
1.6 3.0 79.93
1.7 0.0 0.5026
1.7 0.1 0.5764
1.7 0.2 0.5634
1.7 0.3 0.9777
1.7 0.4 2.082
1.7 0.5 4.177
1.7 0.6 7.268
1.7 0.7 11.15
1.7 0.8 15.25
1.7 0.9 18.94
1.7 1.0 21.25
1.7 1.1 22.49
1.7 1.2 21.59
1.7 1.3 19.32
1.7 1.4 15.68
1.7 1.5 11.44
1.7 1.6 7.756
1.7 1.7 4.877
1.7 1.8 3.707
1.7 1.9 4.537
1.7 2.0 7.522
1.7 2.1 12.13
1.7 2.2 17.65
1.7 2.3 23.76
1.7 2.4 28.95
1.7 2.5 35.1
1.7 2.6 41.56
1.7 2.7 49.23
1.7 2.8 58.11
1.7 2.9 68.55
1.7 3.0 79.97
1.8 0.0 1.337
1.8 0.1 0.5966
1.8 0.2 0.5802
1.8 0.3 1.006
1.8 0.4 2.186
1.8 0.5 4.391
1.8 0.6 7.55
1.8 0.7 11.58
1.8 0.8 15.65
1.8 0.9 19.69
1.8 1.0 22.21
1.8 1.1 23.3
1.8 1.2 22.63
1.8 1.3 19.99
1.8 1.4 16.02
1.8 1.5 11.74
1.8 1.6 7.699
1.8 1.7 4.752
1.8 1.8 3.615
1.8 1.9 4.778
1.8 2.0 8.353
1.8 2.1 14.13
1.8 2.2 21.35
1.8 2.3 29.06
1.8 2.4 36.73
1.8 2.5 43.51
1.8 2.6 49.43
1.8 2.7 55.27
1.8 2.8 62.22
1.8 2.9 70.87
1.8 3.0 80.68
1.9 0.0 1.378
1.9 0.1 0.6032
1.9 0.2 0.5913
1.9 0.3 1.028
1.9 0.4 2.231
1.9 0.5 4.51
1.9 0.6 7.875
1.9 0.7 11.98
1.9 0.8 16.27
1.9 0.9 20.05
1.9 1.0 23.18
1.9 1.1 24.41
1.9 1.2 22.93
1.9 1.3 20.67
1.9 1.4 16.42
1.9 1.5 11.98
1.9 1.6 7.846
1.9 1.7 4.718
1.9 1.8 3.554
1.9 1.9 5.041
1.9 2.0 9.134
1.9 2.1 15.72
1.9 2.2 24.36
1.9 2.3 34.29
1.9 2.4 44.25
1.9 2.5 53.07
1.9 2.6 60.24
1.9 2.7 66.88
1.9 2.8 72.09
1.9 2.9 77.85
1.9 3.0 85.24
2.0 0.0 0.5311
2.0 0.1 0.6163
2.0 0.2 0.596
2.0 0.3 1.057
2.0 0.4 2.308
2.0 0.5 4.638
2.0 0.6 7.927
2.0 0.7 12.23
2.0 0.8 16.69
2.0 0.9 20.77
2.0 1.0 23.76
2.0 1.1 24.7
2.0 1.2 23.94
2.0 1.3 21.01
2.0 1.4 16.78
2.0 1.5 12.21
2.0 1.6 7.714
2.0 1.7 4.655
2.0 1.8 3.648
2.0 1.9 5.216
2.0 2.0 9.621
2.0 2.1 17.01
2.0 2.2 27.02
2.0 2.3 38.74
2.0 2.4 50.97
2.0 2.5 62.49
2.0 2.6 74.26
2.0 2.7 81.46
2.0 2.8 87.38
2.0 2.9 91.18
2.0 3.0 94.19
2.1 0.0 1.23
2.1 0.1 0.6192
2.1 0.2 0.6041
2.1 0.3 1.056
2.1 0.4 2.321
2.1 0.5 4.741
2.1 0.6 8.266
2.1 0.7 12.54
2.1 0.8 17.34
2.1 0.9 21.43
2.1 1.0 24.47
2.1 1.1 25.64
2.1 1.2 24.2
2.1 1.3 21.44
2.1 1.4 17.31
2.1 1.5 12.25
2.1 1.6 7.737
2.1 1.7 4.563
2.1 1.8 3.66
2.1 1.9 5.289
2.1 2.0 10.16
2.1 2.1 18.08
2.1 2.2 29.13
2.1 2.3 41.85
2.1 2.4 56.54
2.1 2.5 71.31
2.1 2.6 84.97
2.1 2.7 95.93
2.1 2.8 104.7
2.1 2.9 108.7
2.1 3.0 109.6
2.2 0.0 0.4648
2.2 0.1 0.6026
2.2 0.2 0.5884
2.2 0.3 1.058
2.2 0.4 2.374
2.2 0.5 4.758
2.2 0.6 8.43
2.2 0.7 12.89
2.2 0.8 17.77
2.2 0.9 21.91
2.2 1.0 24.9
2.2 1.1 26.11
2.2 1.2 24.5
2.2 1.3 21.86
2.2 1.4 17.6
2.2 1.5 12.44
2.2 1.6 7.893
2.2 1.7 4.617
2.2 1.8 3.591
2.2 1.9 5.473
2.2 2.0 10.61
2.2 2.1 18.98
2.2 2.2 30.36
2.2 2.3 44.63
2.2 2.4 60.92
2.2 2.5 76.91
2.2 2.6 94.41
2.2 2.7 109.7
2.2 2.8 121.8
2.2 2.9 128.5
2.2 3.0 132.5
2.3 0.0 0.4926
2.3 0.1 0.5868
2.3 0.2 0.5859
2.3 0.3 1.064
2.3 0.4 2.41
2.3 0.5 4.957
2.3 0.6 8.541
2.3 0.7 13.13
2.3 0.8 18.03
2.3 0.9 22.35
2.3 1.0 25.53
2.3 1.1 26.85
2.3 1.2 25.41
2.3 1.3 22.69
2.3 1.4 17.67
2.3 1.5 12.54
2.3 1.6 7.867
2.3 1.7 4.531
2.3 1.8 3.525
2.3 1.9 5.603
2.3 2.0 10.95
2.3 2.1 19.69
2.3 2.2 31.39
2.3 2.3 46.84
2.3 2.4 62.97
2.3 2.5 82.13
2.3 2.6 100.6
2.3 2.7 117.8
2.3 2.8 134.3
2.3 2.9 148.0
2.3 3.0 155.1
2.4 0.0 0.5843
2.4 0.1 0.5834
2.4 0.2 0.5713
2.4 0.3 1.055
2.4 0.4 2.416
2.4 0.5 4.985
2.4 0.6 8.786
2.4 0.7 13.28
2.4 0.8 18.3
2.4 0.9 22.93
2.4 1.0 25.71
2.4 1.1 26.71
2.4 1.2 26.13
2.4 1.3 22.71
2.4 1.4 17.83
2.4 1.5 12.63
2.4 1.6 7.876
2.4 1.7 4.608
2.4 1.8 3.616
2.4 1.9 5.76
2.4 2.0 11.32
2.4 2.1 20.4
2.4 2.2 32.86
2.4 2.3 47.53
2.4 2.4 66.0
2.4 2.5 85.05
2.4 2.6 105.0
2.4 2.7 125.8
2.4 2.8 145.6
2.4 2.9 161.9
2.4 3.0 173.5
2.5 0.0 0.5158
2.5 0.1 1.092
2.5 0.2 1.614
2.5 0.3 2.612
2.5 0.4 4.562
2.5 0.5 7.614
2.5 0.6 12.14
2.5 0.7 17.16
2.5 0.8 22.9
2.5 0.9 27.61
2.5 1.0 30.92
2.5 1.1 32.64
2.5 1.2 31.44
2.5 1.3 28.41
2.5 1.4 23.84
2.5 1.5 18.82
2.5 1.6 14.23
2.5 1.7 11.28
2.5 1.8 10.64
2.5 1.9 13.28
2.5 2.0 19.54
2.5 2.1 29.5
2.5 2.2 42.47
2.5 2.3 58.92
2.5 2.4 76.99
2.5 2.5 99.23
2.5 2.6 117.4
2.5 2.7 143.0
2.5 2.8 165.1
2.5 2.9 186.8
2.5 3.0 198.4
2.6 0.0 1.293
2.6 0.1 1.087
2.6 0.2 1.603
2.6 0.3 2.665
2.6 0.4 4.623
2.6 0.5 7.821
2.6 0.6 12.17
2.6 0.7 17.57
2.6 0.8 23.0
2.6 0.9 27.96
2.6 1.0 31.39
2.6 1.1 32.93
2.6 1.2 31.7
2.6 1.3 28.62
2.6 1.4 24.08
2.6 1.5 18.8
2.6 1.6 14.18
2.6 1.7 11.21
2.6 1.8 10.85
2.6 1.9 13.61
2.6 2.0 20.02
2.6 2.1 30.04
2.6 2.2 43.45
2.6 2.3 59.9
2.6 2.4 78.41
2.6 2.5 101.3
2.6 2.6 122.3
2.6 2.7 145.6
2.6 2.8 169.0
2.6 2.9 192.9
2.6 3.0 210.1
2.7 0.0 0.5174
2.7 0.1 0.5405
2.7 0.2 0.5451
2.7 0.3 1.072
2.7 0.4 2.512
2.7 0.5 5.177
2.7 0.6 9.134
2.7 0.7 14.03
2.7 0.8 19.24
2.7 0.9 23.71
2.7 1.0 26.96
2.7 1.1 28.24
2.7 1.2 26.91
2.7 1.3 23.64
2.7 1.4 18.56
2.7 1.5 13.01
2.7 1.6 7.912
2.7 1.7 4.555
2.7 1.8 3.685
2.7 1.9 6.026
2.7 2.0 12.12
2.7 2.1 21.71
2.7 2.2 35.19
2.7 2.3 50.88
2.7 2.4 70.71
2.7 2.5 92.82
2.7 2.6 114.2
2.7 2.7 137.4
2.7 2.8 160.1
2.7 2.9 182.5
2.7 3.0 203.4
2.8 0.0 1.181
2.8 0.1 0.5328
2.8 0.2 0.5252
2.8 0.3 1.049
2.8 0.4 2.514
2.8 0.5 5.246
2.8 0.6 9.131
2.8 0.7 14.17
2.8 0.8 19.45
2.8 0.9 24.13
2.8 1.0 27.44
2.8 1.1 28.42
2.8 1.2 27.59
2.8 1.3 23.62
2.8 1.4 18.44
2.8 1.5 13.19
2.8 1.6 7.987
2.8 1.7 4.516
2.8 1.8 3.602
2.8 1.9 6.076
2.8 2.0 12.11
2.8 2.1 21.58
2.8 2.2 35.26
2.8 2.3 52.04
2.8 2.4 72.44
2.8 2.5 93.25
2.8 2.6 116.1
2.8 2.7 140.6
2.8 2.8 163.7
2.8 2.9 186.7
2.8 3.0 205.8
2.9 0.0 0.5997
2.9 0.1 0.5231
2.9 0.2 0.5135
2.9 0.3 1.048
2.9 0.4 2.529
2.9 0.5 5.249
2.9 0.6 9.2
2.9 0.7 14.31
2.9 0.8 19.56
2.9 0.9 24.29
2.9 1.0 27.68
2.9 1.1 28.56
2.9 1.2 27.55
2.9 1.3 23.97
2.9 1.4 18.9
2.9 1.5 13.12
2.9 1.6 7.86
2.9 1.7 4.473
2.9 1.8 3.682
2.9 1.9 6.159
2.9 2.0 12.28
2.9 2.1 22.37
2.9 2.2 35.74
2.9 2.3 53.46
2.9 2.4 71.99
2.9 2.5 93.62
2.9 2.6 118.6
2.9 2.7 141.2
2.9 2.8 168.6
2.9 2.9 190.0
2.9 3.0 210.6
3.0 0.0 0.6083
3.0 0.1 0.5111
3.0 0.2 0.5047
3.0 0.3 1.059
3.0 0.4 2.532
3.0 0.5 5.28
3.0 0.6 9.381
3.0 0.7 14.51
3.0 0.8 19.55
3.0 0.9 24.45
3.0 1.0 27.9
3.0 1.1 28.98
3.0 1.2 27.41
3.0 1.3 23.84
3.0 1.4 18.97
3.0 1.5 13.06
3.0 1.6 7.942
3.0 1.7 4.498
3.0 1.8 3.641
3.0 1.9 6.272
3.0 2.0 12.44
3.0 2.1 22.62
3.0 2.2 35.91
3.0 2.3 53.07
3.0 2.4 73.29
3.0 2.5 95.03
3.0 2.6 118.0
3.0 2.7 144.8
3.0 2.8 168.8
3.0 2.9 191.9
3.0 3.0 214.6

Answer 1

我相信你要找的是contourf函数，而不是轮廓函数，尽管它们的工作方式相似。

要了解更多信息，请查看http://matplotlib.org/examples/pylab_examples/contourf_demo.html

上的matplotlib文档