I am using the PyVista package to resample a .vtk mesh of nonconforming rectangular prisms onto a grid of points. However, some of the points fail to sample the dataset, which results in a "0" value in the index of that point in pointset_sample.point_data['vtkValidPointMask'] and either 0 or some NaN color value when plotting the points.
Here's a minimal reproducible example:
mesh.vtk:
# vtk DataFile Version 2.0
MeshData
ASCII
DATASET UNSTRUCTURED_GRID
POINTS 207 float
-2500 -2500 -400
-1500 -2500 -400
-500 -2500 -400
500 -2500 -400
1500 -2500 -400
2500 -2500 -400
-2500 -1500 -400
-1500 -1500 -400
-500 -1500 -400
500 -1500 -400
1500 -1500 -400
2500 -1500 -400
-2500 -500 -400
-1500 -500 -400
-500 -500 -400
500 -500 -400
1500 -500 -400
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-2500 500 -400
-1500 500 -400
-500 500 -400
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1500 500 -400
2500 500 -400
-2500 1500 -400
-1500 1500 -400
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500 1500 -400
1500 1500 -400
2500 1500 -400
-2500 2500 -400
-1500 2500 -400
-500 2500 -400
500 2500 -400
1500 2500 -400
2500 2500 -400
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-1500 -500 0
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-2500 500 0
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-2500 1500 0
-1500 1500 0
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-2500 -2500 1600
-1500 -2500 1600
-500 -2500 1600
500 -2500 1600
1500 -2500 1600
2500 -2500 1600
-2500 -1500 1600
-1500 -1500 1600
-500 -1500 1600
500 -1500 1600
1500 -1500 1600
2500 -1500 1600
-2500 -500 1600
-1500 -500 1600
-500 -500 1600
500 -500 1600
1500 -500 1600
2500 -500 1600
-2500 500 1600
-1500 500 1600
-500 500 1600
500 500 1600
1500 500 1600
2500 500 1600
-2500 1500 1600
-1500 1500 1600
-500 1500 1600
500 1500 1600
1500 1500 1600
2500 1500 1600
-2500 2500 1600
-1500 2500 1600
-500 2500 1600
500 2500 1600
1500 2500 1600
2500 2500 1600
-1000 -1500 -400
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-1000 -1500 0
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-1000 -500 0
0 -1500 -400
0 -1000 -400
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1000 -1500 0
1000 -1000 0
1500 -1000 0
1000 -500 0
-1500 0 -400
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-500 0 -400
-1000 500 -400
-1500 0 0
-1000 0 0
-500 0 0
-1000 500 0
0 0 -400
500 0 -400
0 500 -400
0 0 0
500 0 0
0 500 0
1000 0 -400
1500 0 -400
1000 500 -400
1000 0 0
1500 0 0
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-1500 1000 -400
-1000 1000 -400
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-1000 1500 -400
-1500 1000 0
-1000 1000 0
-500 1000 0
-1000 1500 0
0 1000 -400
500 1000 -400
0 1500 -400
0 1000 0
500 1000 0
0 1500 0
1000 1000 -400
1500 1000 -400
1000 1500 -400
1000 1000 0
1500 1000 0
1000 1500 0
-1000 -1500 1600
-1500 -1000 1600
-1000 -1000 1600
-500 -1000 1600
-1000 -500 1600
0 -1500 1600
0 -1000 1600
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0 -500 1600
1000 -1500 1600
1000 -1000 1600
1500 -1000 1600
1000 -500 1600
-1500 0 1600
-1000 0 1600
-500 0 1600
-1000 500 1600
0 0 1600
500 0 1600
0 500 1600
1000 0 1600
1500 0 1600
1000 500 1600
-1500 1000 1600
-1000 1000 1600
-500 1000 1600
-1000 1500 1600
0 1000 1600
500 1000 1600
0 1500 1600
1000 1000 1600
1500 1000 1600
1000 1500 1600
CELLS 104 936
8 0 1 7 6 36 37 43 42
8 1 2 8 7 37 38 44 43
8 2 3 9 8 38 39 45 44
8 3 4 10 9 39 40 46 45
8 4 5 11 10 40 41 47 46
8 6 7 13 12 42 43 49 48
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8 36 37 43 42 72 73 79 78
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8 42 43 49 48 78 79 85 84
8 46 47 53 52 82 83 89 88
8 48 49 55 54 84 85 91 90
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8 64 65 71 70 100 101 107 106
8 7 108 110 109 43 113 115 114
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8 109 110 112 13 114 115 117 49
8 110 111 14 112 115 116 50 117
8 8 118 119 111 44 122 123 116
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8 111 119 121 14 116 123 125 50
8 119 120 15 121 123 124 51 125
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8 127 128 16 129 131 132 52 133
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8 134 135 137 19 138 139 141 55
8 135 136 20 137 139 140 56 141
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8 19 137 155 154 55 141 159 158
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8 155 156 26 157 159 160 62 161
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8 156 162 164 26 160 165 167 62
8 162 163 27 164 165 166 63 167
8 21 150 168 163 57 153 171 166
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8 163 168 170 27 166 171 173 63
8 168 169 28 170 171 172 64 173
8 43 113 115 114 79 174 176 175
8 113 44 116 115 174 80 177 176
8 114 115 117 49 175 176 178 85
8 115 116 50 117 176 177 86 178
8 44 122 123 116 80 179 180 177
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8 123 124 51 125 180 181 87 182
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8 130 46 132 131 183 82 185 184
8 124 131 133 51 181 184 186 87
8 131 132 52 133 184 185 88 186
8 49 117 139 138 85 178 188 187
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8 138 139 141 55 187 188 190 91
8 139 140 56 141 188 189 92 190
8 50 125 145 140 86 182 191 189
8 125 51 146 145 182 87 192 191
8 140 145 147 56 189 191 193 92
8 145 146 57 147 191 192 93 193
8 51 133 151 146 87 186 194 192
8 133 52 152 151 186 88 195 194
8 146 151 153 57 192 194 196 93
8 151 152 58 153 194 195 94 196
8 55 141 159 158 91 190 198 197
8 141 56 160 159 190 92 199 198
8 158 159 161 61 197 198 200 97
8 159 160 62 161 198 199 98 200
8 56 147 165 160 92 193 201 199
8 147 57 166 165 193 93 202 201
8 160 165 167 62 199 201 203 98
8 165 166 63 167 201 202 99 203
8 57 153 171 166 93 196 204 202
8 153 58 172 171 196 94 205 204
8 166 171 173 63 202 204 206 99
8 171 172 64 173 204 205 100 206
CELL_TYPES 104
12
12
12
12
12
12
12
12
12
12
12
12
12
12
12
12
12
12
12
12
12
12
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12
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12
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12
12
12
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12
12
12
12
12
12
12
12
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12
12
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12
12
12
12
12
12
12
12
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12
12
12
12
12
12
12
12
12
12
12
12
12
12
12
12
12
12
12
12
12
12
12
12
12
12
12
12
12
12
12
12
12
12
12
12
12
12
12
12
12
12
12
12
12
12
12
12
CELL_DATA 104
SCALARS BlockID int
LOOKUP_TABLE default
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
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
0
0
0
0
0
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
SCALARS Resistivity[Ohm-m] float
LOOKUP_TABLE default
1e+10
1e+10
1e+10
1e+10
1e+10
1e+10
1e+10
1e+10
1e+10
1e+10
1e+10
1e+10
1e+10
1e+10
1e+10
1e+10
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
1e+10
1e+10
1e+10
1e+10
1e+10
1e+10
1e+10
1e+10
1e+10
1e+10
1e+10
1e+10
1e+10
1e+10
1e+10
1e+10
1e+10
1e+10
1e+10
1e+10
1e+10
1e+10
1e+10
1e+10
1e+10
1e+10
1e+10
1e+10
1e+10
1e+10
1e+10
1e+10
1e+10
1e+10
1e+10
1e+10
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
SCALARS ElemID int
LOOKUP_TABLE default
0
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
SCALARS Level int
LOOKUP_TABLE default
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
0
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
SCALARS Volume float
LOOKUP_TABLE default
0.4
0.4
0.4
0.4
0.4
0.4
0.4
0.4
0.4
0.4
0.4
0.4
0.4
0.4
0.4
0.4
1.6
1.6
1.6
1.6
1.6
1.6
1.6
1.6
1.6
1.6
1.6
1.6
1.6
1.6
1.6
1.6
0.1
0.1
0.1
0.1
0.1
0.1
0.1
0.1
0.1
0.1
0.1
0.1
0.1
0.1
0.1
0.1
0.1
0.1
0.1
0.1
0.1
0.1
0.1
0.1
0.1
0.1
0.1
0.1
0.1
0.1
0.1
0.1
0.1
0.1
0.1
0.1
0.4
0.4
0.4
0.4
0.4
0.4
0.4
0.4
0.4
0.4
0.4
0.4
0.4
0.4
0.4
0.4
0.4
0.4
0.4
0.4
0.4
0.4
0.4
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0.4
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0.4
0.4
0.4
0.4
0.4
0.4
0.4
0.4
0.4
POINT_DATA 207
SCALARS NodeID int
LOOKUP_TABLE default
0
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
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25
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28
29
30
31
32
33
34
35
36
37
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41
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44
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113
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120
121
122
123
124
125
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128
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133
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142
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206
array in points.py:
import numpy as np
points = np.array([[-1.912e+03, -1.912e+03, -2.000e+02],
[-1.912e+03, -1.912e+03, 8.000e+02],
[-1.030e+03, -1.912e+03, -2.000e+02],
[-1.030e+03, -1.912e+03, 8.000e+02],
[-5.890e+02, -1.912e+03, -2.000e+02],
[-5.890e+02, -1.912e+03, 8.000e+02],
[-2.950e+02, -1.912e+03, -2.000e+02],
[-2.950e+02, -1.912e+03, 8.000e+02],
[-1.000e+00, -1.912e+03, -2.000e+02],
[-1.000e+00, -1.912e+03, 8.000e+02],
[ 2.930e+02, -1.912e+03, -2.000e+02],
[ 2.930e+02, -1.912e+03, 8.000e+02],
[ 5.870e+02, -1.912e+03, -2.000e+02],
[ 5.870e+02, -1.912e+03, 8.000e+02],
[ 1.028e+03, -1.912e+03, -2.000e+02],
[ 1.028e+03, -1.912e+03, 8.000e+02],
[ 1.910e+03, -1.912e+03, -2.000e+02],
[ 1.910e+03, -1.912e+03, 8.000e+02],
[-1.912e+03, -1.030e+03, -2.000e+02],
[-1.912e+03, -1.030e+03, 8.000e+02],
[-1.030e+03, -1.030e+03, -2.000e+02],
[-1.030e+03, -1.030e+03, 8.000e+02],
[-5.890e+02, -1.030e+03, -2.000e+02],
[-5.890e+02, -1.030e+03, 8.000e+02],
[-2.950e+02, -1.030e+03, -2.000e+02],
[-2.950e+02, -1.030e+03, 8.000e+02],
[-1.000e+00, -1.030e+03, -2.000e+02],
[-1.000e+00, -1.030e+03, 8.000e+02],
[ 2.930e+02, -1.030e+03, -2.000e+02],
[ 2.930e+02, -1.030e+03, 8.000e+02],
[ 5.870e+02, -1.030e+03, -2.000e+02],
[ 5.870e+02, -1.030e+03, 8.000e+02],
[ 1.028e+03, -1.030e+03, -2.000e+02],
[ 1.028e+03, -1.030e+03, 8.000e+02],
[ 1.910e+03, -1.030e+03, -2.000e+02],
[ 1.910e+03, -1.030e+03, 8.000e+02],
[-1.912e+03, -5.890e+02, -2.000e+02],
[-1.912e+03, -5.890e+02, 8.000e+02],
[-1.030e+03, -5.890e+02, -2.000e+02],
[-1.030e+03, -5.890e+02, 8.000e+02],
[-5.890e+02, -5.890e+02, -2.000e+02],
[-5.890e+02, -5.890e+02, 8.000e+02],
[-2.950e+02, -5.890e+02, -2.000e+02],
[-2.950e+02, -5.890e+02, 8.000e+02],
[-1.000e+00, -5.890e+02, -2.000e+02],
[-1.000e+00, -5.890e+02, 8.000e+02],
[ 2.930e+02, -5.890e+02, -2.000e+02],
[ 2.930e+02, -5.890e+02, 8.000e+02],
[ 5.870e+02, -5.890e+02, -2.000e+02],
[ 5.870e+02, -5.890e+02, 8.000e+02],
[ 1.028e+03, -5.890e+02, -2.000e+02],
[ 1.028e+03, -5.890e+02, 8.000e+02],
[ 1.910e+03, -5.890e+02, -2.000e+02],
[ 1.910e+03, -5.890e+02, 8.000e+02],
[-1.912e+03, -2.950e+02, -2.000e+02],
[-1.912e+03, -2.950e+02, 8.000e+02],
[-1.030e+03, -2.950e+02, -2.000e+02],
[-1.030e+03, -2.950e+02, 8.000e+02],
[-5.890e+02, -2.950e+02, -2.000e+02],
[-5.890e+02, -2.950e+02, 8.000e+02],
[-2.950e+02, -2.950e+02, -2.000e+02],
[-2.950e+02, -2.950e+02, 8.000e+02],
[-1.000e+00, -2.950e+02, -2.000e+02],
[-1.000e+00, -2.950e+02, 8.000e+02],
[ 2.930e+02, -2.950e+02, -2.000e+02],
[ 2.930e+02, -2.950e+02, 8.000e+02],
[ 5.870e+02, -2.950e+02, -2.000e+02],
[ 5.870e+02, -2.950e+02, 8.000e+02],
[ 1.028e+03, -2.950e+02, -2.000e+02],
[ 1.028e+03, -2.950e+02, 8.000e+02],
[ 1.910e+03, -2.950e+02, -2.000e+02],
[ 1.910e+03, -2.950e+02, 8.000e+02],
[-1.912e+03, -1.000e+00, -2.000e+02],
[-1.912e+03, -1.000e+00, 8.000e+02],
[-1.030e+03, -1.000e+00, -2.000e+02],
[-1.030e+03, -1.000e+00, 8.000e+02],
[-5.890e+02, -1.000e+00, -2.000e+02],
[-5.890e+02, -1.000e+00, 8.000e+02],
[-2.950e+02, -1.000e+00, -2.000e+02],
[-2.950e+02, -1.000e+00, 8.000e+02],
[-1.000e+00, -1.000e+00, -2.000e+02],
[-1.000e+00, -1.000e+00, 8.000e+02],
[ 2.930e+02, -1.000e+00, -2.000e+02],
[ 2.930e+02, -1.000e+00, 8.000e+02],
[ 5.870e+02, -1.000e+00, -2.000e+02],
[ 5.870e+02, -1.000e+00, 8.000e+02],
[ 1.028e+03, -1.000e+00, -2.000e+02],
[ 1.028e+03, -1.000e+00, 8.000e+02],
[ 1.910e+03, -1.000e+00, -2.000e+02],
[ 1.910e+03, -1.000e+00, 8.000e+02],
[-1.912e+03, 2.930e+02, -2.000e+02],
[-1.912e+03, 2.930e+02, 8.000e+02],
[-1.030e+03, 2.930e+02, -2.000e+02],
[-1.030e+03, 2.930e+02, 8.000e+02],
[-5.890e+02, 2.930e+02, -2.000e+02],
[-5.890e+02, 2.930e+02, 8.000e+02],
[-2.950e+02, 2.930e+02, -2.000e+02],
[-2.950e+02, 2.930e+02, 8.000e+02],
[-1.000e+00, 2.930e+02, -2.000e+02],
[-1.000e+00, 2.930e+02, 8.000e+02],
[ 2.930e+02, 2.930e+02, -2.000e+02],
[ 2.930e+02, 2.930e+02, 8.000e+02],
[ 5.870e+02, 2.930e+02, -2.000e+02],
[ 5.870e+02, 2.930e+02, 8.000e+02],
[ 1.028e+03, 2.930e+02, -2.000e+02],
[ 1.028e+03, 2.930e+02, 8.000e+02],
[ 1.910e+03, 2.930e+02, -2.000e+02],
[ 1.910e+03, 2.930e+02, 8.000e+02],
[-1.912e+03, 5.870e+02, -2.000e+02],
[-1.912e+03, 5.870e+02, 8.000e+02],
[-1.030e+03, 5.870e+02, -2.000e+02],
[-1.030e+03, 5.870e+02, 8.000e+02],
[-5.890e+02, 5.870e+02, -2.000e+02],
[-5.890e+02, 5.870e+02, 8.000e+02],
[-2.950e+02, 5.870e+02, -2.000e+02],
[-2.950e+02, 5.870e+02, 8.000e+02],
[-1.000e+00, 5.870e+02, -2.000e+02],
[-1.000e+00, 5.870e+02, 8.000e+02],
[ 2.930e+02, 5.870e+02, -2.000e+02],
[ 2.930e+02, 5.870e+02, 8.000e+02],
[ 5.870e+02, 5.870e+02, -2.000e+02],
[ 5.870e+02, 5.870e+02, 8.000e+02],
[ 1.028e+03, 5.870e+02, -2.000e+02],
[ 1.028e+03, 5.870e+02, 8.000e+02],
[ 1.910e+03, 5.870e+02, -2.000e+02],
[ 1.910e+03, 5.870e+02, 8.000e+02],
[-1.912e+03, 1.028e+03, -2.000e+02],
[-1.912e+03, 1.028e+03, 8.000e+02],
[-1.030e+03, 1.028e+03, -2.000e+02],
[-1.030e+03, 1.028e+03, 8.000e+02],
[-5.890e+02, 1.028e+03, -2.000e+02],
[-5.890e+02, 1.028e+03, 8.000e+02],
[-2.950e+02, 1.028e+03, -2.000e+02],
[-2.950e+02, 1.028e+03, 8.000e+02],
[-1.000e+00, 1.028e+03, -2.000e+02],
[-1.000e+00, 1.028e+03, 8.000e+02],
[ 2.930e+02, 1.028e+03, -2.000e+02],
[ 2.930e+02, 1.028e+03, 8.000e+02],
[ 5.870e+02, 1.028e+03, -2.000e+02],
[ 5.870e+02, 1.028e+03, 8.000e+02],
[ 1.028e+03, 1.028e+03, -2.000e+02],
[ 1.028e+03, 1.028e+03, 8.000e+02],
[ 1.910e+03, 1.028e+03, -2.000e+02],
[ 1.910e+03, 1.028e+03, 8.000e+02],
[-1.912e+03, 1.910e+03, -2.000e+02],
[-1.912e+03, 1.910e+03, 8.000e+02],
[-1.030e+03, 1.910e+03, -2.000e+02],
[-1.030e+03, 1.910e+03, 8.000e+02],
[-5.890e+02, 1.910e+03, -2.000e+02],
[-5.890e+02, 1.910e+03, 8.000e+02],
[-2.950e+02, 1.910e+03, -2.000e+02],
[-2.950e+02, 1.910e+03, 8.000e+02],
[-1.000e+00, 1.910e+03, -2.000e+02],
[-1.000e+00, 1.910e+03, 8.000e+02],
[ 2.930e+02, 1.910e+03, -2.000e+02],
[ 2.930e+02, 1.910e+03, 8.000e+02],
[ 5.870e+02, 1.910e+03, -2.000e+02],
[ 5.870e+02, 1.910e+03, 8.000e+02],
[ 1.028e+03, 1.910e+03, -2.000e+02],
[ 1.028e+03, 1.910e+03, 8.000e+02],
[ 1.910e+03, 1.910e+03, -2.000e+02],
[ 1.910e+03, 1.910e+03, 8.000e+02]])
Example code:
import pyvista as pv
import numpy as np
from points import points
mesh = pv.read("mesh.vtk")
mesh.set_active_scalars('Resistivity[Ohm-m]')
pointset = pv.PointSet(points)
pointset_sample = pointset.sample(mesh)
print(pointset_sample.point_data['vtkValidPointMask'])
plot = pv.Plotter()
plot.add_mesh(pointset_sample, scalars = 'Resistivity[Ohm-m]', cmap = 'turbo_r',
render_points_as_spheres=True, point_size=20,
log_scale=True, clim=[5e0, 5e2])
plot.add_mesh(mesh, scalars = 'Resistivity[Ohm-m]', cmap = 'turbo_r',
opacity=0.3, show_edges=True, line_width=2, edge_opacity=1.0,
log_scale=True, clim=[5e0, 5e2])
plot.show()
This results in this plot, where the 12 points that are missed in this example are on the red end of the color ramp, while all others are in agreement with the cells that contain them and are either green or purple, and a vtkValidPointMask of:
[1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1]
What is causing some points to fail sampling an enclosing mesh? How do I prevent points from failing to sample an enclosing mesh?
I can reproduce your issue and I don't know why it happens, but frankly I've never quite understood the subtleties of sample/probe/interpolate and friends, despite multiple attempts of more knowledgeable people to explain these to me :) So I also don't know if any of the other similar filters could be applicable. It might be worth opening an issue for.
What I do know is that mesh.find_closest_cell() seems to work fine for your example. Since you just want to pick out the constant cell scalar for each point (at least based on your example), you can find the closest cell to each point and query the cell data. This might be feasible depending on the size of your actual problem.
Here's the corresponding change:
import pyvista as pv
import numpy as np
from points import points
mesh = pv.read("mesh.vtk")
mesh.set_active_scalars('Resistivity[Ohm-m]')
pointset = pv.PointSet(points)
closest_cell_inds = mesh.find_closest_cell(points)
pointset['Resistivity[Ohm-m]'] = mesh.cell_data['Resistivity[Ohm-m]'][closest_cell_inds]
plot = pv.Plotter()
plot.add_mesh(pointset, scalars='Resistivity[Ohm-m]', cmap='turbo_r',
render_points_as_spheres=True, point_size=20,
log_scale=True, clim=[5e0, 5e2])
plot.add_mesh(mesh, scalars='Resistivity[Ohm-m]', cmap='turbo_r',
opacity=0.3, show_edges=True, line_width=2, edge_opacity=1.0,
log_scale=True, clim=[5e0, 5e2])
plot.show()
Some interactive work on your original sampled data shows that this does the right thing:
>>> closest_cell_inds = mesh.find_closest_cell(pointset_sample.points)
>>> sampling_works = pointset_sample['Resistivity[Ohm-m]'] == mesh.cell_data['Resistivity[Ohm-m]'][closest_cell_inds]
>>> sampling_works
pyvista_ndarray([ True, True, True, True, True, True, True, True,
True, True, True, True, True, True, True, True,
True, True, False, False, True, True, True, True,
True, True, True, True, True, True, True, True,
True, True, False, False, True, True, True, True,
True, True, True, True, True, True, True, True,
True, True, True, True, True, True, True, True,
True, True, True, True, True, True, True, True,
True, True, True, True, True, True, True, True,
False, False, True, True, True, True, True, True,
True, True, True, True, True, True, True, True,
False, False, True, True, True, True, True, True,
True, True, True, True, True, True, True, True,
True, True, True, True, True, True, True, True,
True, True, True, True, True, True, True, True,
True, True, True, True, True, True, False, False,
True, True, True, True, True, True, True, True,
True, True, True, True, True, True, False, False,
True, True, True, True, True, True, True, True,
True, True, True, True, True, True, True, True,
True, True])
>>> (sampling_works == pointset_sample['vtkValidPointMask']).all()
np.True_