Tutorial
– Program Grid_to_Vox
Given are
bivariate (X,Y) scatter data from a series of samples at different geological
ages. Using program Grid2.2.out discrete bivariate frequency distributions DX,DY,Z,F are generated, with DX and DY being the grid-cell sizes of the
X- and Y coordinate axes, respectively, with Z being the geological age of a
particular sample, and with F being the bivariate frequency of points per
grid-cell (see, for example, Knappertsbusch, 2000). The program Grid_to_Vox3 is
reserved for handling the G. menardii data set, while Grid_to_Vox4 is reserved
for the C. leptoporus data set (this separation into two programs was done in
order to facilitate programming efforts).
Input to
Grid_to_Vox:
Both
Grid_to_Vox versions work in batch operating mode, so that a large number of
gridded input files can be processed one after the other. Two types of input
files are required: First, one text file called List_of_files, which contains a
list of the age (in Ma) of the sample and the corresponding name of the file
with the gridded data matrix per sample. The gridded data matrix contains the
frequency distribution of the bivariate set of measurements. The age must be
written in digits of five characters, followed by a comma, followed by the name
of the gridded input matrix. The name of the gidded input data is 16 characters
long. The second type of input files are the files with the gridded data
matrices (one file per sample). The gridded data matrices need to be
unformatted, i.e., without any header or column information (these must first
be removed by manual editing).
Example for
Grid_to_Vox3 (Globorotalia menardii):
File
List_of_files:
00.340,input1xxxxxxxxxx_grd.txt
56.781,input2xxxxxxxxxx_grd.txt
Gridded data
matrix (for Globorotalia menardii):
A 14x16
matrix (14 columns, 16 rows).
DX goes in horizontal direction
(mid-points at 25, 75, 125,..., 675µm).
[Intervals
of dDX=50µm].
DY goes in vertical direction
(mid-points at 50, 150, 250,...,1550µm).
[dDY=100µm].
File input1xxxxx_grid:
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
99 100 101 102 103 104 105 106 107 108 109 110 111 112
113 114 115 116 117 118 119 120 121 122 123 124 125 126
127 128 129 130 131 132 133 134 135 136 137 138 139 140
140 141 142 143 144 145 146 147 148 149 150 151 152 153
154 155 156 157 158 159 160 161 162 163 164 165 166 167
167 168 169 170 171 172 173 174 175 176 177 178 179 180
181 182 183 184 185 186 187 188 189 190 191 192 193 194
195 196 197 198 199 200 201 202 203 204 205 206 207 208
209 210 211 212 213 214 215 216 217 218 219 220 221 222
Output
The format
of the output data, which can be imported in Voxler is
DX, DY, Age (Ma), Frequency
Example for
file input1xxxxx_grid:
25. 50. .34 1.
25. 150. .34
15.
25. 250. .34
29.
25. 350. .34
43.
25. 450. .34
57.
25. 550. .34
71.
25. 650. .34
85.
25. 750. .34
99.
25. 850. .34 113.
25. 950. .34 127.
25.
1050.
.34 140.
25.
1150.
.34 154.
25.
1250.
.34 167.
25.
1350.
.34 181.
25.
1450.
.34 195.
25.
1550.
.34 209.
75. 50. .34 2.
75. 150. .34
16.
75. 250. .34
30.
75. 350. .34
44.
75. 450. .34
58.
75. 550. .34
72.
75. 650. .34
86.
75. 750.
.34 100.
75. 850. .34 114.
75. 950. .34 128.
75.
1050.
.34 141.
75.
1150.
.34 155.
75.
1250.
.34 168.
75.
1350.
.34 182.
75.
1450.
.34 196.
75.
1550.
.34 210.
125. 50. .34 3.
125. 150. .34
17.
125. 250. .34
31.
125. 350. .34
45.
125. 450. .34
59.
125. 550. .34
73.
125. 650. .34
87.
125. 750. .34 101.
125. 850. .34 115.
125. 950. .34 129.
125.
1050.
.34 142.
125.
1150.
.34 156.
125.
1250.
.34 169.
125.
1350.
.34 183.
125.
1450.
.34 197.
125.
1550.
.34 211.
175. 50. .34 4.
175. 150. .34
18.
175. 250. .34
32.
175. 350. .34
46.
175. 450. .34
60.
175. 550. .34
74.
175.
650. .34
88.
175. 750. .34 102.
175. 850. .34 116.
175. 950. .34 130.
175.
1050.
.34 143.
175.
1150.
.34 157.
175.
1250.
.34 170.
175.
1350.
.34 184.
175.
1450.
.34 198.
175.
1550.
.34 212.
225. 50. .34 5.
225. 150. .34
19.
225. 250. .34
33.
225. 350. .34
47.
225. 450. .34
61.
225. 550. .34
75.
225. 650. .34
89.
225. 750. .34 103.
225. 850. .34 117.
225. 950. .34 131.
225.
1050.
.34 144.
225.
1150.
.34 158.
225.
1250.
.34 171.
225.
1350.
.34 185.
225.
1450.
.34 199.
225.
1550.
.34 213.
275. 50. .34 6.
275. 150. .34
20.
275. 250. .34
34.
275. 350. .34
48.
275. 450. .34
62.
275. 550. .34 76.
275. 650. .34
90.
275. 750. .34 104.
275. 850. .34 118.
275. 950. .34 132.
275.
1050.
.34 145.
275.
1150.
.34 159.
275.
1250.
.34 172.
275.
1350.
.34 186.
275.
1450.
.34 200.
275.
1550.
.34 214.
325. 50. .34 7.
325. 150. .34
21.
325. 250. .34
35.
325. 350. .34
49.
325. 450. .34
63.
325. 550. .34
77.
325. 650. .34
91.
325. 750. .34 105.
325. 850. .34 119.
325. 950. .34 133.
325.
1050.
.34 146.
325.
1150.
.34 160.
325.
1250.
.34 173.
325.
1350.
.34 187.
325.
1450.
.34 201.
325.
1550.
.34 215.
375. 50. .34 8.
375. 150. .34
22.
375. 250. .34
36.
375. 350. .34
50.
375. 450. .34
64.
375. 550. .34
78.
375. 650. .34
92.
375. 750. .34 106.
375. 850. .34 120.
375. 950. .34 134.
375.
1050.
.34 147.
375.
1150.
.34 161.
375.
1250.
.34 174.
375.
1350.
.34 188.
375.
1450.
.34 202.
375.
1550.
.34 216.
425. 50. .34 9.
425. 150. .34
23.
425. 250. .34
37.
425. 350. .34
51.
425. 450. .34
65.
425. 550. .34
79.
425. 650. .34
93.
425. 750. .34 107.
425. 850. .34 121.
425. 950. .34 135.
425.
1050.
.34 148.
425.
1150.
.34 162.
425.
1250.
.34 175.
425.
1350.
.34 189.
425.
1450.
.34 203.
425.
1550.
.34 217.
475. 50. .34
10.
475. 150. .34
24.
475. 250. .34
38.
475. 350. .34
52.
475. 450. .34
66.
475. 550. .34
80.
475. 650. .34
94.
475. 750. .34 108.
475. 850. .34 122.
475. 950. .34 136.
475.
1050.
.34 149.
475.
1150.
.34 163.
475.
1250.
.34 176.
475.
1350.
.34 190.
475.
1450.
.34 204.
475.
1550.
.34 218.
525. 50. .34
11.
525. 150. .34
25.
525. 250. .34
39.
525. 350. .34
53.
525. 450. .34
67.
525. 550. .34
81.
525. 650. .34
95.
525. 750. .34 109.
525. 850. .34 123.
525. 950. .34 137.
525.
1050.
.34 150.
525.
1150.
.34 164.
525.
1250.
.34 177.
525.
1350.
.34 191.
525.
1450.
.34 205.
525.
1550.
.34 219.
575. 50. .34
12.
575. 150. .34
26.
575. 250. .34
40.
575. 350. .34
54.
575. 450. .34
68.
575. 550. .34
82.
575. 650. .34
96.
575. 750. .34 110.
575. 850. .34 124.
575. 950. .34 138.
575.
1050.
.34 151.
575.
1150.
.34 165.
575.
1250.
.34 178.
575.
1350.
.34 192.
575.
1450.
.34 206.
575.
1550.
.34 220.
625. 50. .34
13.
625. 150. .34
27.
625. 250. .34
41.
625. 350. .34
55.
625. 450. .34
69.
625. 550. .34
83.
625. 650. .34
97.
625. 750. .34 111.
625. 850. .34 125.
625. 950. .34 139.
625.
1050.
.34 152.
625.
1150.
.34 166.
625.
1250.
.34 179.
625.
1350.
.34 193.
625.
1450.
.34 207.
625.
1550.
.34 221.
675. 50. .34
14.
675. 150. .34
28.
675. 250. .34
42.
675. 350. .34
56.
675. 450. .34
70.
675. 550. .34
84.
675. 650. .34
98.
675. 750. .34 112.
675. 850. .34 126.
675. 950. .34 140.
675.
1050.
.34 153.
675.
1150.
.34 167.
675.
1250.
.34 180.
675.
1350.
.34 194.
675.
1450.
.34 208.
675.
1550.
.34 222.