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I have a problem in the annexed excel file there are two tables, first table give the graph representing geological layers I need to get the second table with NaN values

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Dear Please I need a help to get NaN of graph of excel file (annexed file) representing gelogical layers using If-condition (the change in depths of each layer Put NaN value to made a gaps as shown in the second table) .i.e. replace first change level-values in each curve of depth .

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Voss
Voss 2023 年 2 月 9 日
t = readtable('data.xlsx');
t1 = t(:,1:12); % first table from file
t2 = t(:,end-11:end); % second table from file
disp(t1);
xc gBt z1 z2 z3 z4 z5 z6 z7 z8 z9 z10 ______ ______ ___ ___ ___ ___ ___ ___ ___ ___ ___ ___ 0 60.791 0.4 1.3 1.8 2.1 2.8 2.9 3.2 3.5 3.8 4.7 1.6613 60.558 0.4 1.3 1.8 2.1 2.8 2.9 3.2 3.5 3.8 4.7 3.3225 60.325 0.4 1.3 1.8 2.1 2.8 2.9 3.2 3.5 3.8 4.7 4.9838 60.098 0.3 1.2 1.7 2 2.7 2.8 3.1 3.4 3.7 4.6 6.6451 59.875 0.3 1.2 1.7 1.9 2.6 2.7 3 3.3 3.6 4.5 8.3063 59.659 0.3 1.2 1.7 1.9 2.6 2.7 3 3.3 3.6 4.5 9.9676 59.444 0.3 1.2 1.7 1.9 2.6 2.7 3 3.3 3.6 4.5 11.629 59.234 0.3 1.2 1.7 1.9 2.6 2.7 3 3.3 3.6 4.5 13.29 59.031 0.3 1.2 1.7 1.9 2.6 2.7 3 3.3 3.6 4.5 14.951 58.832 0.3 1.1 1.6 1.8 2.5 2.6 2.9 3.2 3.5 4.4 16.613 58.639 0.3 1.1 1.6 1.8 2.5 2.6 2.9 3.2 3.5 4.4 18.274 58.446 0.3 1.1 1.6 1.8 2.5 2.6 2.9 3.2 3.5 4.4 19.935 58.263 0.3 1.1 1.6 1.8 2.5 2.6 2.9 3.2 3.5 4.4 21.596 58.083 0.3 1.1 1.6 1.8 2.5 2.6 2.9 3.2 3.5 4.4 23.258 57.907 0.3 1.1 1.6 1.8 2.5 2.6 2.9 3.2 3.5 4.4 24.919 57.731 0.3 1.1 1.6 1.8 2.5 2.6 2.9 3.2 3.5 4.4 26.58 57.56 0.3 1.1 1.6 1.8 2.5 2.6 2.9 3.2 3.5 4.4 28.242 57.395 0.3 1.1 1.6 1.8 2.4 2.5 2.8 3.1 3.4 4.3 29.903 57.231 0.3 1.1 1.6 1.8 2.4 2.5 2.8 3.1 3.4 4.3 31.564 57.068 0.3 1.1 1.6 1.8 2.4 2.5 2.8 3.1 3.4 4.3 33.225 56.906 0.3 1.1 1.6 1.8 2.4 2.5 2.8 3.1 3.4 4.3 34.887 56.753 0.3 1.1 1.6 1.8 2.4 2.5 2.8 3.1 3.4 4.3 36.548 56.602 0.3 1.1 1.6 1.8 2.4 2.5 2.8 3.1 3.4 4.3 38.209 56.45 0.3 1.1 1.6 1.8 2.4 2.5 2.8 3.1 3.4 4.3 39.87 56.301 0.3 1.1 1.6 1.8 2.4 2.5 2.8 3.1 3.4 4.3 41.532 56.156 0.3 1.1 1.6 1.8 2.4 2.5 2.8 3.1 3.4 4.3 43.193 56.017 0.3 1.1 1.6 1.8 2.4 2.5 2.8 3.1 3.4 4.3 44.854 55.874 0.3 1.1 1.6 1.8 2.4 2.5 2.8 3.1 3.4 4.2 46.516 55.737 0.3 1.1 1.6 1.8 2.4 2.5 2.8 3.1 3.4 4.2 48.177 55.602 0.3 1.1 1.6 1.8 2.4 2.5 2.8 3.1 3.4 4.2 49.838 55.471 0.3 1.1 1.6 1.8 2.4 2.5 2.8 3.1 3.4 4.2 51.499 55.339 0.3 1.1 1.6 1.8 2.4 2.5 2.8 3.1 3.4 4.2 53.161 55.209 0.3 1.1 1.6 1.8 2.4 2.5 2.8 3.1 3.4 4.2 54.822 55.082 0.3 1.1 1.6 1.8 2.4 2.5 2.8 3.1 3.4 4.2 56.483 54.956 0.3 1.1 1.6 1.8 2.4 2.5 2.8 3.1 3.4 4.2 58.144 54.833 0.3 1.1 1.5 1.7 2.3 2.4 2.7 3 3.3 4.1 59.806 54.707 0.3 1.1 1.5 1.7 2.3 2.4 2.7 3 3.3 4.1 61.467 54.587 0.3 1.1 1.5 1.7 2.3 2.4 2.7 3 3.3 4.1 63.128 54.467 0.3 1.1 1.5 1.7 2.3 2.4 2.7 3 3.3 4.1 64.789 54.35 0.3 1.1 1.5 1.7 2.3 2.4 2.7 3 3.3 4.1 66.451 54.233 0.3 1.1 1.5 1.7 2.3 2.4 2.7 3 3.3 4.1 68.112 54.122 0.3 1.1 1.5 1.7 2.3 2.4 2.6 2.9 3.2 4 69.773 54.019 0.3 1.1 1.5 1.7 2.3 2.4 2.6 2.9 3.2 4 71.435 53.919 0.3 1.1 1.5 1.7 2.3 2.4 2.6 2.9 3.2 4 73.096 53.829 0.3 1.1 1.5 1.7 2.3 2.4 2.6 2.9 3.2 4 74.757 53.739 0.3 1.1 1.5 1.7 2.3 2.4 2.6 2.9 3.2 4 76.418 53.656 0.3 1.1 1.5 1.7 2.3 2.4 2.6 2.9 3.2 4 78.08 53.575 0.3 1.1 1.5 1.7 2.3 2.4 2.6 2.9 3.2 4 79.741 53.494 0.3 1.1 1.5 1.7 2.3 2.4 2.6 2.9 3.2 4 81.402 53.416 0.3 1.1 1.5 1.7 2.3 2.4 2.6 2.9 3.2 4 83.063 53.341 0.3 1.1 1.5 1.7 2.3 2.4 2.6 2.9 3.2 4 84.725 53.27 0.3 1.1 1.5 1.7 2.3 2.4 2.6 2.9 3.2 4 86.386 53.198 0.3 1.1 1.5 1.7 2.3 2.4 2.6 2.9 3.2 4 88.047 53.128 0.3 1.1 1.5 1.7 2.3 2.4 2.6 2.9 3.2 4 89.708 53.062 0.3 1.1 1.5 1.7 2.3 2.4 2.6 2.9 3.2 4 91.37 52.996 0.3 1.1 1.5 1.7 2.3 2.4 2.6 2.9 3.2 4 93.031 52.931 0.3 1.1 1.5 1.7 2.3 2.4 2.6 2.9 3.2 4 94.692 52.863 0.3 1.1 1.5 1.7 2.3 2.4 2.6 2.9 3.2 4 96.354 52.795 0.3 1.1 1.5 1.7 2.3 2.4 2.6 2.9 3.2 4 98.015 52.727 0.3 1.1 1.5 1.7 2.3 2.4 2.6 2.9 3.2 4 99.676 52.657 0.3 1.1 1.5 1.7 2.3 2.4 2.6 2.9 3.2 4 101.34 52.582 0.3 1.1 1.5 1.7 2.3 2.4 2.6 2.9 3.2 4 103 52.514 0.3 1.1 1.5 1.7 2.3 2.4 2.6 2.9 3.2 4 104.66 52.447 0.3 1.1 1.5 1.7 2.3 2.4 2.6 2.9 3.2 4 106.32 52.389 0.3 1.1 1.5 1.7 2.3 2.4 2.6 2.9 3.2 4 107.98 52.332 0.3 1.1 1.5 1.7 2.3 2.4 2.6 2.9 3.2 4 109.64 52.281 0.3 1.1 1.5 1.7 2.3 2.4 2.6 2.9 3.2 4 111.3 52.237 0.3 1.1 1.5 1.7 2.3 2.4 2.6 2.9 3.2 4 112.97 52.2 0.3 1.1 1.5 1.7 2.3 2.4 2.6 2.9 3.2 4 114.63 52.184 0.3 1.1 1.5 1.7 2.3 2.4 2.6 2.9 3.2 4 116.29 52.172 0.3 1.1 1.5 1.7 2.3 2.4 2.6 2.9 3.2 4 117.95 52.173 0.3 1.1 1.5 1.7 2.3 2.4 2.6 2.9 3.2 4 119.61 52.177 0.3 1.1 1.5 1.7 2.3 2.4 2.6 2.9 3.2 4 121.27 52.188 0.3 1.1 1.5 1.7 2.3 2.4 2.6 2.9 3.2 4 122.93 52.201 0.3 1.1 1.5 1.7 2.3 2.4 2.6 2.9 3.2 4 124.6 52.223 0.3 1.1 1.5 1.7 2.3 2.4 2.6 2.9 3.2 4 126.26 52.257 0.3 1.1 1.5 1.7 2.3 2.4 2.6 2.9 3.2 4 127.92 52.297 0.3 1.1 1.5 1.7 2.3 2.4 2.6 2.9 3.2 4 129.58 52.346 0.3 1.1 1.5 1.7 2.3 2.4 2.6 2.9 3.2 4 131.24 52.397 0.3 1.1 1.5 1.7 2.3 2.4 2.6 2.9 3.2 4 132.9 52.453 0.3 1.1 1.5 1.7 2.3 2.4 2.6 2.9 3.2 4 134.56 52.513 0.3 1.1 1.5 1.7 2.3 2.4 2.6 2.9 3.2 4 136.22 52.574 0.3 1.1 1.5 1.7 2.3 2.4 2.6 2.9 3.2 4 137.89 52.64 0.3 1.1 1.5 1.7 2.3 2.4 2.6 2.9 3.2 4 139.55 52.708 0.3 1.1 1.5 1.7 2.3 2.4 2.6 2.9 3.2 4 141.21 52.779 0.3 1.1 1.5 1.7 2.3 2.4 2.6 2.9 3.2 4 142.87 52.852 0.3 1.1 1.5 1.7 2.3 2.4 2.6 2.9 3.2 4 144.53 52.924 0.3 1.1 1.5 1.7 2.3 2.4 2.6 2.9 3.2 4 146.19 52.999 0.3 1.1 1.5 1.7 2.3 2.4 2.6 2.9 3.2 4 147.85 53.073 0.3 1.1 1.5 1.7 2.3 2.4 2.6 2.9 3.2 4 149.51 53.146 0.3 1.1 1.5 1.7 2.3 2.4 2.6 2.9 3.2 4 151.18 53.217 0.3 1.1 1.5 1.7 2.3 2.4 2.6 2.9 3.2 4 152.84 53.287 0.3 1.1 1.5 1.7 2.3 2.4 2.6 2.9 3.2 4 154.5 53.356 0.3 1.1 1.5 1.7 2.3 2.4 2.6 2.9 3.2 4 156.16 53.42 0.3 1.1 1.5 1.7 2.3 2.4 2.6 2.9 3.2 4 157.82 53.483 0.3 1.1 1.5 1.7 2.3 2.4 2.6 2.9 3.2 4 159.48 53.543 0.3 1.1 1.5 1.7 2.3 2.4 2.6 2.9 3.2 4 161.14 53.605 0.3 1.1 1.5 1.7 2.3 2.4 2.6 2.9 3.2 4 162.8 53.659 0.3 1.1 1.5 1.7 2.3 2.4 2.6 2.9 3.2 4 164.47 53.709 0.3 1.1 1.5 1.7 2.3 2.4 2.6 2.9 3.2 4 166.13 53.748 0.3 1.1 1.5 1.7 2.3 2.4 2.6 2.9 3.2 4 167.79 53.783 0.3 1.1 1.5 1.7 2.3 2.4 2.6 2.9 3.2 4 169.45 53.815 0.3 1.1 1.5 1.7 2.3 2.4 2.6 2.9 3.2 4 171.11 53.841 0.3 1.1 1.5 1.7 2.3 2.4 2.6 2.9 3.2 4 172.77 53.87 0.3 1.1 1.5 1.7 2.3 2.4 2.6 2.9 3.2 4 174.43 53.895 0.3 1.1 1.5 1.7 2.3 2.4 2.6 2.9 3.2 4 176.09 53.924 0.3 1.1 1.5 1.7 2.3 2.4 2.6 2.9 3.2 4 177.76 53.95 0.3 1.1 1.5 1.7 2.3 2.4 2.6 2.9 3.2 4 179.42 53.978 0.3 1.1 1.5 1.7 2.3 2.4 2.6 2.9 3.2 4 181.08 54.008 0.3 1.1 1.5 1.7 2.3 2.4 2.6 2.9 3.2 4 182.74 54.041 0.3 1.1 1.5 1.7 2.3 2.4 2.6 2.9 3.2 4 184.4 54.075 0.3 1.1 1.5 1.7 2.3 2.4 2.6 2.9 3.2 4 186.06 54.108 0.3 1.1 1.5 1.7 2.3 2.4 2.6 2.9 3.2 4 187.72 54.143 0.3 1.1 1.5 1.7 2.3 2.4 2.6 2.9 3.2 4 189.38 54.176 0.3 1.1 1.5 1.7 2.3 2.4 2.7 3 3.3 4.1 191.05 54.209 0.3 1.1 1.5 1.7 2.3 2.4 2.7 3 3.3 4.1 192.71 54.241 0.3 1.1 1.5 1.7 2.3 2.4 2.7 3 3.3 4.1 194.37 54.272 0.3 1.1 1.5 1.7 2.3 2.4 2.7 3 3.3 4.1 196.03 54.3 0.3 1.1 1.5 1.7 2.3 2.4 2.7 3 3.3 4.1 197.69 54.325 0.3 1.1 1.5 1.7 2.3 2.4 2.7 3 3.3 4.1 199.35 54.348 0.3 1.1 1.5 1.7 2.3 2.4 2.7 3 3.3 4.1 201.01 54.366 0.3 1.1 1.5 1.7 2.3 2.4 2.7 3 3.3 4.1 202.67 54.384 0.3 1.1 1.5 1.7 2.3 2.4 2.7 3 3.3 4.1 204.34 54.397 0.3 1.1 1.5 1.7 2.3 2.4 2.7 3 3.3 4.1 206 54.407 0.3 1.1 1.5 1.7 2.3 2.4 2.7 3 3.3 4.1 207.66 54.413 0.3 1.1 1.5 1.7 2.3 2.4 2.7 3 3.3 4.1 209.32 54.419 0.3 1.1 1.5 1.7 2.3 2.4 2.7 3 3.3 4.1 210.98 54.421 0.3 1.1 1.5 1.7 2.3 2.4 2.7 3 3.3 4.1 212.64 54.417 0.3 1.1 1.5 1.7 2.3 2.4 2.7 3 3.3 4.1 214.3 54.412 0.3 1.1 1.5 1.7 2.3 2.4 2.7 3 3.3 4.1 215.96 54.404 0.3 1.1 1.5 1.7 2.3 2.4 2.7 3 3.3 4.1 217.63 54.396 0.3 1.1 1.5 1.7 2.3 2.4 2.7 3 3.3 4.1 219.29 54.378 0.3 1.1 1.5 1.7 2.3 2.4 2.7 3 3.3 4.1 220.95 54.36 0.3 1.1 1.5 1.7 2.3 2.4 2.7 3 3.3 4.1 222.61 54.334 0.3 1.1 1.5 1.7 2.3 2.4 2.7 3 3.3 4.1 224.27 54.302 0.3 1.1 1.5 1.7 2.3 2.4 2.7 3 3.3 4.1 225.93 54.263 0.3 1.1 1.5 1.7 2.3 2.4 2.7 3 3.3 4.1 227.59 54.217 0.3 1.1 1.5 1.7 2.3 2.4 2.7 3 3.3 4.1 229.25 54.168 0.3 1.1 1.5 1.7 2.3 2.4 2.7 3 3.3 4.1 230.92 54.111 0.3 1.1 1.5 1.7 2.3 2.4 2.6 2.9 3.2 4 232.58 54.046 0.3 1.1 1.5 1.7 2.3 2.4 2.6 2.9 3.2 4 234.24 53.967 0.3 1.1 1.5 1.7 2.3 2.4 2.6 2.9 3.2 4 235.9 53.884 0.3 1.1 1.5 1.7 2.3 2.4 2.6 2.9 3.2 4 237.56 53.784 0.3 1.1 1.5 1.7 2.3 2.4 2.6 2.9 3.2 4 239.22 53.672 0.3 1.1 1.5 1.7 2.3 2.4 2.6 2.9 3.2 4 240.88 53.552 0.3 1.1 1.5 1.7 2.3 2.4 2.6 2.9 3.2 4 242.55 53.423 0.3 1.1 1.5 1.7 2.3 2.4 2.6 2.9 3.2 4 244.21 53.294 0.3 1.1 1.5 1.7 2.3 2.4 2.6 2.9 3.2 4 245.87 53.153 0.3 1.1 1.5 1.7 2.3 2.4 2.6 2.9 3.2 4 247.53 53.01 0.3 1.1 1.5 1.7 2.3 2.4 2.6 2.9 3.2 4 249.19 52.858 0.3 1.1 1.5 1.7 2.3 2.4 2.6 2.9 3.2 4 250.85 52.704 0.3 1.1 1.5 1.7 2.3 2.4 2.6 2.9 3.2 4 252.51 52.545 0.3 1.1 1.5 1.7 2.3 2.4 2.6 2.9 3.2 4 254.17 52.381 0.3 1.1 1.5 1.7 2.3 2.4 2.6 2.9 3.2 4 255.84 52.217 0.3 1.1 1.5 1.7 2.3 2.4 2.6 2.9 3.2 4 257.5 52.049 0.3 1.1 1.5 1.7 2.3 2.4 2.6 2.9 3.2 4 259.16 51.883 0.3 1 1.4 1.6 2.2 2.3 2.5 2.8 3.1 3.9 260.82 51.711 0.3 1 1.4 1.6 2.2 2.3 2.5 2.8 3.1 3.9 262.48 51.541 0.3 1 1.4 1.6 2.2 2.3 2.5 2.8 3.1 3.9 264.14 51.37 0.3 1 1.4 1.6 2.2 2.3 2.5 2.8 3.1 3.9 265.8 51.201 0.3 1 1.4 1.6 2.2 2.3 2.5 2.8 3.1 3.9 267.46 51.026 0.3 1 1.4 1.6 2.2 2.3 2.5 2.8 3.1 3.9 269.13 50.849 0.3 1 1.4 1.6 2.2 2.3 2.5 2.8 3.1 3.9 270.79 50.671 0.3 1 1.4 1.6 2.2 2.3 2.5 2.8 3.1 3.9 272.45 50.486 0.3 1 1.4 1.6 2.2 2.3 2.5 2.8 3.1 3.9 274.11 50.296 0.3 1 1.4 1.6 2.2 2.3 2.5 2.8 3.1 3.9 275.77 50.09 0.3 1 1.4 1.6 2.2 2.3 2.5 2.8 3.1 3.9 277.43 49.88 0.3 1 1.4 1.6 2.2 2.3 2.5 2.8 3.1 3.9 279.09 49.657 0.3 1 1.4 1.6 2.2 2.3 2.5 2.8 3.1 3.9 280.75 49.426 0.3 1 1.4 1.6 2.2 2.3 2.5 2.8 3.1 3.9 282.42 49.183 0.3 1 1.4 1.6 2.2 2.3 2.5 2.8 3.1 3.8 284.08 48.932 0.3 1 1.4 1.6 2.2 2.3 2.5 2.8 3.1 3.8 285.74 48.68 0.3 1 1.4 1.6 2.2 2.3 2.5 2.8 3.1 3.8 287.4 48.418 0.3 1 1.4 1.6 2.1 2.2 2.4 2.7 3 3.7 289.06 48.155 0.3 1 1.4 1.6 2.1 2.2 2.4 2.7 3 3.7 290.72 47.888 0.3 1 1.4 1.6 2.1 2.2 2.4 2.7 3 3.7 292.38 47.623 0.3 1 1.4 1.6 2.1 2.2 2.4 2.7 3 3.7 294.04 47.355 0.3 1 1.4 1.6 2.1 2.2 2.4 2.7 3 3.7 295.71 47.082 0.3 1 1.4 1.6 2.1 2.2 2.4 2.7 3 3.7 297.37 46.806 0.3 1 1.4 1.6 2.1 2.2 2.4 2.7 3 3.7 299.03 46.528 0.3 1 1.4 1.6 2.1 2.2 2.4 2.7 2.9 3.6 300.69 46.25 0.3 1 1.4 1.6 2.1 2.2 2.4 2.7 2.9 3.6 302.35 45.963 0.3 1 1.4 1.6 2.1 2.2 2.4 2.7 2.9 3.6 304.01 45.678 0.3 1 1.4 1.6 2.1 2.2 2.4 2.7 2.9 3.6 305.67 45.386 0.3 1 1.4 1.6 2.1 2.2 2.4 2.7 2.9 3.6 307.33 45.099 0.3 1 1.4 1.6 2.1 2.2 2.4 2.6 2.8 3.5 309 44.808 0.3 0.9 1.3 1.5 2 2.1 2.3 2.5 2.7 3.4 310.66 44.518 0.3 0.9 1.3 1.5 2 2.1 2.3 2.5 2.7 3.4 312.32 44.23 0.3 0.9 1.3 1.5 2 2.1 2.3 2.5 2.7 3.4 313.98 43.937 0.3 0.9 1.3 1.5 2 2.1 2.3 2.5 2.7 3.4 315.64 43.646 0.3 0.9 1.3 1.5 2 2.1 2.3 2.5 2.7 3.4 317.3 43.354 0.3 0.9 1.3 1.5 2 2.1 2.3 2.5 2.7 3.4 318.96 43.068 0.2 0.8 1.2 1.4 1.9 2 2.2 2.4 2.6 3.3 320.62 42.792 0.2 0.8 1.2 1.4 1.9 2 2.2 2.4 2.6 3.3 322.29 42.523 0.2 0.8 1.1 1.3 1.8 1.9 2.1 2.3 2.5 3.1 323.95 42.264 0.2 0.8 1.1 1.3 1.8 1.9 2.1 2.3 2.5 3.1 325.61 42.018 0.2 0.8 1.1 1.3 1.8 1.9 2.1 2.3 2.5 3.1 327.27 41.785 0.2 0.8 1.1 1.3 1.8 1.9 2.1 2.3 2.5 3.1 328.93 41.568 0.2 0.8 1.1 1.3 1.8 1.9 2.1 2.3 2.5 3.1 330.59 41.355 0.2 0.8 1.1 1.3 1.8 1.9 2.1 2.3 2.5 3.1 332.25 41.159 0.2 0.8 1.1 1.3 1.8 1.9 2.1 2.3 2.5 3.1 332.25 41.159 0.2 0.8 1.1 1.3 1.8 1.9 2.1 2.3 2.5 3.1
The second table seems (more-or-less) to be the result of placing NaNs in the cells of the first table containing the last of each set of contiguous equal values in a column, e.g.:
1
1
1 % <- replace last 1 with NaN
2
2
2
However, there are a few instances where the NaNs were placed in cells containing the first of a set of contiguous equal values in a column, e.g.:
1
1
1
2 % <- replace first 2 with NaN
2
2
Your question mentions "first change level-values", which suggests the second approach, but the actual table is more like the first approach.
So here's how you can do both approaches, and you can take your pick (the functions are defined below):
t1_bottoms = t1;
t1_bottoms(:,3:end) = varfun(@place_NaNs_at_bottoms_of_layers,t1,'InputVariables',3:size(t1,2));
disp(t1_bottoms);
xc gBt z1 z2 z3 z4 z5 z6 z7 z8 z9 z10 ______ ______ ___ ___ ___ ___ ___ ___ ___ ___ ___ ___ 0 60.791 0.4 1.3 1.8 2.1 2.8 2.9 3.2 3.5 3.8 4.7 1.6613 60.558 0.4 1.3 1.8 2.1 2.8 2.9 3.2 3.5 3.8 4.7 3.3225 60.325 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 4.9838 60.098 0.3 1.2 1.7 NaN NaN NaN NaN NaN NaN NaN 6.6451 59.875 0.3 1.2 1.7 1.9 2.6 2.7 3 3.3 3.6 4.5 8.3063 59.659 0.3 1.2 1.7 1.9 2.6 2.7 3 3.3 3.6 4.5 9.9676 59.444 0.3 1.2 1.7 1.9 2.6 2.7 3 3.3 3.6 4.5 11.629 59.234 0.3 1.2 1.7 1.9 2.6 2.7 3 3.3 3.6 4.5 13.29 59.031 0.3 NaN NaN NaN NaN NaN NaN NaN NaN NaN 14.951 58.832 0.3 1.1 1.6 1.8 2.5 2.6 2.9 3.2 3.5 4.4 16.613 58.639 0.3 1.1 1.6 1.8 2.5 2.6 2.9 3.2 3.5 4.4 18.274 58.446 0.3 1.1 1.6 1.8 2.5 2.6 2.9 3.2 3.5 4.4 19.935 58.263 0.3 1.1 1.6 1.8 2.5 2.6 2.9 3.2 3.5 4.4 21.596 58.083 0.3 1.1 1.6 1.8 2.5 2.6 2.9 3.2 3.5 4.4 23.258 57.907 0.3 1.1 1.6 1.8 2.5 2.6 2.9 3.2 3.5 4.4 24.919 57.731 0.3 1.1 1.6 1.8 2.5 2.6 2.9 3.2 3.5 4.4 26.58 57.56 0.3 1.1 1.6 1.8 NaN NaN NaN NaN NaN NaN 28.242 57.395 0.3 1.1 1.6 1.8 2.4 2.5 2.8 3.1 3.4 4.3 29.903 57.231 0.3 1.1 1.6 1.8 2.4 2.5 2.8 3.1 3.4 4.3 31.564 57.068 0.3 1.1 1.6 1.8 2.4 2.5 2.8 3.1 3.4 4.3 33.225 56.906 0.3 1.1 1.6 1.8 2.4 2.5 2.8 3.1 3.4 4.3 34.887 56.753 0.3 1.1 1.6 1.8 2.4 2.5 2.8 3.1 3.4 4.3 36.548 56.602 0.3 1.1 1.6 1.8 2.4 2.5 2.8 3.1 3.4 4.3 38.209 56.45 0.3 1.1 1.6 1.8 2.4 2.5 2.8 3.1 3.4 4.3 39.87 56.301 0.3 1.1 1.6 1.8 2.4 2.5 2.8 3.1 3.4 4.3 41.532 56.156 0.3 1.1 1.6 1.8 2.4 2.5 2.8 3.1 3.4 4.3 43.193 56.017 0.3 1.1 1.6 1.8 2.4 2.5 2.8 3.1 3.4 NaN 44.854 55.874 0.3 1.1 1.6 1.8 2.4 2.5 2.8 3.1 3.4 4.2 46.516 55.737 0.3 1.1 1.6 1.8 2.4 2.5 2.8 3.1 3.4 4.2 48.177 55.602 0.3 1.1 1.6 1.8 2.4 2.5 2.8 3.1 3.4 4.2 49.838 55.471 0.3 1.1 1.6 1.8 2.4 2.5 2.8 3.1 3.4 4.2 51.499 55.339 0.3 1.1 1.6 1.8 2.4 2.5 2.8 3.1 3.4 4.2 53.161 55.209 0.3 1.1 1.6 1.8 2.4 2.5 2.8 3.1 3.4 4.2 54.822 55.082 0.3 1.1 1.6 1.8 2.4 2.5 2.8 3.1 3.4 4.2 56.483 54.956 0.3 1.1 NaN NaN NaN NaN NaN NaN NaN NaN 58.144 54.833 0.3 1.1 1.5 1.7 2.3 2.4 2.7 3 3.3 4.1 59.806 54.707 0.3 1.1 1.5 1.7 2.3 2.4 2.7 3 3.3 4.1 61.467 54.587 0.3 1.1 1.5 1.7 2.3 2.4 2.7 3 3.3 4.1 63.128 54.467 0.3 1.1 1.5 1.7 2.3 2.4 2.7 3 3.3 4.1 64.789 54.35 0.3 1.1 1.5 1.7 2.3 2.4 2.7 3 3.3 4.1 66.451 54.233 0.3 1.1 1.5 1.7 2.3 2.4 NaN NaN NaN NaN 68.112 54.122 0.3 1.1 1.5 1.7 2.3 2.4 2.6 2.9 3.2 4 69.773 54.019 0.3 1.1 1.5 1.7 2.3 2.4 2.6 2.9 3.2 4 71.435 53.919 0.3 1.1 1.5 1.7 2.3 2.4 2.6 2.9 3.2 4 73.096 53.829 0.3 1.1 1.5 1.7 2.3 2.4 2.6 2.9 3.2 4 74.757 53.739 0.3 1.1 1.5 1.7 2.3 2.4 2.6 2.9 3.2 4 76.418 53.656 0.3 1.1 1.5 1.7 2.3 2.4 2.6 2.9 3.2 4 78.08 53.575 0.3 1.1 1.5 1.7 2.3 2.4 2.6 2.9 3.2 4 79.741 53.494 0.3 1.1 1.5 1.7 2.3 2.4 2.6 2.9 3.2 4 81.402 53.416 0.3 1.1 1.5 1.7 2.3 2.4 2.6 2.9 3.2 4 83.063 53.341 0.3 1.1 1.5 1.7 2.3 2.4 2.6 2.9 3.2 4 84.725 53.27 0.3 1.1 1.5 1.7 2.3 2.4 2.6 2.9 3.2 4 86.386 53.198 0.3 1.1 1.5 1.7 2.3 2.4 2.6 2.9 3.2 4 88.047 53.128 0.3 1.1 1.5 1.7 2.3 2.4 2.6 2.9 3.2 4 89.708 53.062 0.3 1.1 1.5 1.7 2.3 2.4 2.6 2.9 3.2 4 91.37 52.996 0.3 1.1 1.5 1.7 2.3 2.4 2.6 2.9 3.2 4 93.031 52.931 0.3 1.1 1.5 1.7 2.3 2.4 2.6 2.9 3.2 4 94.692 52.863 0.3 1.1 1.5 1.7 2.3 2.4 2.6 2.9 3.2 4 96.354 52.795 0.3 1.1 1.5 1.7 2.3 2.4 2.6 2.9 3.2 4 98.015 52.727 0.3 1.1 1.5 1.7 2.3 2.4 2.6 2.9 3.2 4 99.676 52.657 0.3 1.1 1.5 1.7 2.3 2.4 2.6 2.9 3.2 4 101.34 52.582 0.3 1.1 1.5 1.7 2.3 2.4 2.6 2.9 3.2 4 103 52.514 0.3 1.1 1.5 1.7 2.3 2.4 2.6 2.9 3.2 4 104.66 52.447 0.3 1.1 1.5 1.7 2.3 2.4 2.6 2.9 3.2 4 106.32 52.389 0.3 1.1 1.5 1.7 2.3 2.4 2.6 2.9 3.2 4 107.98 52.332 0.3 1.1 1.5 1.7 2.3 2.4 2.6 2.9 3.2 4 109.64 52.281 0.3 1.1 1.5 1.7 2.3 2.4 2.6 2.9 3.2 4 111.3 52.237 0.3 1.1 1.5 1.7 2.3 2.4 2.6 2.9 3.2 4 112.97 52.2 0.3 1.1 1.5 1.7 2.3 2.4 2.6 2.9 3.2 4 114.63 52.184 0.3 1.1 1.5 1.7 2.3 2.4 2.6 2.9 3.2 4 116.29 52.172 0.3 1.1 1.5 1.7 2.3 2.4 2.6 2.9 3.2 4 117.95 52.173 0.3 1.1 1.5 1.7 2.3 2.4 2.6 2.9 3.2 4 119.61 52.177 0.3 1.1 1.5 1.7 2.3 2.4 2.6 2.9 3.2 4 121.27 52.188 0.3 1.1 1.5 1.7 2.3 2.4 2.6 2.9 3.2 4 122.93 52.201 0.3 1.1 1.5 1.7 2.3 2.4 2.6 2.9 3.2 4 124.6 52.223 0.3 1.1 1.5 1.7 2.3 2.4 2.6 2.9 3.2 4 126.26 52.257 0.3 1.1 1.5 1.7 2.3 2.4 2.6 2.9 3.2 4 127.92 52.297 0.3 1.1 1.5 1.7 2.3 2.4 2.6 2.9 3.2 4 129.58 52.346 0.3 1.1 1.5 1.7 2.3 2.4 2.6 2.9 3.2 4 131.24 52.397 0.3 1.1 1.5 1.7 2.3 2.4 2.6 2.9 3.2 4 132.9 52.453 0.3 1.1 1.5 1.7 2.3 2.4 2.6 2.9 3.2 4 134.56 52.513 0.3 1.1 1.5 1.7 2.3 2.4 2.6 2.9 3.2 4 136.22 52.574 0.3 1.1 1.5 1.7 2.3 2.4 2.6 2.9 3.2 4 137.89 52.64 0.3 1.1 1.5 1.7 2.3 2.4 2.6 2.9 3.2 4 139.55 52.708 0.3 1.1 1.5 1.7 2.3 2.4 2.6 2.9 3.2 4 141.21 52.779 0.3 1.1 1.5 1.7 2.3 2.4 2.6 2.9 3.2 4 142.87 52.852 0.3 1.1 1.5 1.7 2.3 2.4 2.6 2.9 3.2 4 144.53 52.924 0.3 1.1 1.5 1.7 2.3 2.4 2.6 2.9 3.2 4 146.19 52.999 0.3 1.1 1.5 1.7 2.3 2.4 2.6 2.9 3.2 4 147.85 53.073 0.3 1.1 1.5 1.7 2.3 2.4 2.6 2.9 3.2 4 149.51 53.146 0.3 1.1 1.5 1.7 2.3 2.4 2.6 2.9 3.2 4 151.18 53.217 0.3 1.1 1.5 1.7 2.3 2.4 2.6 2.9 3.2 4 152.84 53.287 0.3 1.1 1.5 1.7 2.3 2.4 2.6 2.9 3.2 4 154.5 53.356 0.3 1.1 1.5 1.7 2.3 2.4 2.6 2.9 3.2 4 156.16 53.42 0.3 1.1 1.5 1.7 2.3 2.4 2.6 2.9 3.2 4 157.82 53.483 0.3 1.1 1.5 1.7 2.3 2.4 2.6 2.9 3.2 4 159.48 53.543 0.3 1.1 1.5 1.7 2.3 2.4 2.6 2.9 3.2 4 161.14 53.605 0.3 1.1 1.5 1.7 2.3 2.4 2.6 2.9 3.2 4 162.8 53.659 0.3 1.1 1.5 1.7 2.3 2.4 2.6 2.9 3.2 4 164.47 53.709 0.3 1.1 1.5 1.7 2.3 2.4 2.6 2.9 3.2 4 166.13 53.748 0.3 1.1 1.5 1.7 2.3 2.4 2.6 2.9 3.2 4 167.79 53.783 0.3 1.1 1.5 1.7 2.3 2.4 2.6 2.9 3.2 4 169.45 53.815 0.3 1.1 1.5 1.7 2.3 2.4 2.6 2.9 3.2 4 171.11 53.841 0.3 1.1 1.5 1.7 2.3 2.4 2.6 2.9 3.2 4 172.77 53.87 0.3 1.1 1.5 1.7 2.3 2.4 2.6 2.9 3.2 4 174.43 53.895 0.3 1.1 1.5 1.7 2.3 2.4 2.6 2.9 3.2 4 176.09 53.924 0.3 1.1 1.5 1.7 2.3 2.4 2.6 2.9 3.2 4 177.76 53.95 0.3 1.1 1.5 1.7 2.3 2.4 2.6 2.9 3.2 4 179.42 53.978 0.3 1.1 1.5 1.7 2.3 2.4 2.6 2.9 3.2 4 181.08 54.008 0.3 1.1 1.5 1.7 2.3 2.4 2.6 2.9 3.2 4 182.74 54.041 0.3 1.1 1.5 1.7 2.3 2.4 2.6 2.9 3.2 4 184.4 54.075 0.3 1.1 1.5 1.7 2.3 2.4 2.6 2.9 3.2 4 186.06 54.108 0.3 1.1 1.5 1.7 2.3 2.4 2.6 2.9 3.2 4 187.72 54.143 0.3 1.1 1.5 1.7 2.3 2.4 NaN NaN NaN NaN 189.38 54.176 0.3 1.1 1.5 1.7 2.3 2.4 2.7 3 3.3 4.1 191.05 54.209 0.3 1.1 1.5 1.7 2.3 2.4 2.7 3 3.3 4.1 192.71 54.241 0.3 1.1 1.5 1.7 2.3 2.4 2.7 3 3.3 4.1 194.37 54.272 0.3 1.1 1.5 1.7 2.3 2.4 2.7 3 3.3 4.1 196.03 54.3 0.3 1.1 1.5 1.7 2.3 2.4 2.7 3 3.3 4.1 197.69 54.325 0.3 1.1 1.5 1.7 2.3 2.4 2.7 3 3.3 4.1 199.35 54.348 0.3 1.1 1.5 1.7 2.3 2.4 2.7 3 3.3 4.1 201.01 54.366 0.3 1.1 1.5 1.7 2.3 2.4 2.7 3 3.3 4.1 202.67 54.384 0.3 1.1 1.5 1.7 2.3 2.4 2.7 3 3.3 4.1 204.34 54.397 0.3 1.1 1.5 1.7 2.3 2.4 2.7 3 3.3 4.1 206 54.407 0.3 1.1 1.5 1.7 2.3 2.4 2.7 3 3.3 4.1 207.66 54.413 0.3 1.1 1.5 1.7 2.3 2.4 2.7 3 3.3 4.1 209.32 54.419 0.3 1.1 1.5 1.7 2.3 2.4 2.7 3 3.3 4.1 210.98 54.421 0.3 1.1 1.5 1.7 2.3 2.4 2.7 3 3.3 4.1 212.64 54.417 0.3 1.1 1.5 1.7 2.3 2.4 2.7 3 3.3 4.1 214.3 54.412 0.3 1.1 1.5 1.7 2.3 2.4 2.7 3 3.3 4.1 215.96 54.404 0.3 1.1 1.5 1.7 2.3 2.4 2.7 3 3.3 4.1 217.63 54.396 0.3 1.1 1.5 1.7 2.3 2.4 2.7 3 3.3 4.1 219.29 54.378 0.3 1.1 1.5 1.7 2.3 2.4 2.7 3 3.3 4.1 220.95 54.36 0.3 1.1 1.5 1.7 2.3 2.4 2.7 3 3.3 4.1 222.61 54.334 0.3 1.1 1.5 1.7 2.3 2.4 2.7 3 3.3 4.1 224.27 54.302 0.3 1.1 1.5 1.7 2.3 2.4 2.7 3 3.3 4.1 225.93 54.263 0.3 1.1 1.5 1.7 2.3 2.4 2.7 3 3.3 4.1 227.59 54.217 0.3 1.1 1.5 1.7 2.3 2.4 2.7 3 3.3 4.1 229.25 54.168 0.3 1.1 1.5 1.7 2.3 2.4 NaN NaN NaN NaN 230.92 54.111 0.3 1.1 1.5 1.7 2.3 2.4 2.6 2.9 3.2 4 232.58 54.046 0.3 1.1 1.5 1.7 2.3 2.4 2.6 2.9 3.2 4 234.24 53.967 0.3 1.1 1.5 1.7 2.3 2.4 2.6 2.9 3.2 4 235.9 53.884 0.3 1.1 1.5 1.7 2.3 2.4 2.6 2.9 3.2 4 237.56 53.784 0.3 1.1 1.5 1.7 2.3 2.4 2.6 2.9 3.2 4 239.22 53.672 0.3 1.1 1.5 1.7 2.3 2.4 2.6 2.9 3.2 4 240.88 53.552 0.3 1.1 1.5 1.7 2.3 2.4 2.6 2.9 3.2 4 242.55 53.423 0.3 1.1 1.5 1.7 2.3 2.4 2.6 2.9 3.2 4 244.21 53.294 0.3 1.1 1.5 1.7 2.3 2.4 2.6 2.9 3.2 4 245.87 53.153 0.3 1.1 1.5 1.7 2.3 2.4 2.6 2.9 3.2 4 247.53 53.01 0.3 1.1 1.5 1.7 2.3 2.4 2.6 2.9 3.2 4 249.19 52.858 0.3 1.1 1.5 1.7 2.3 2.4 2.6 2.9 3.2 4 250.85 52.704 0.3 1.1 1.5 1.7 2.3 2.4 2.6 2.9 3.2 4 252.51 52.545 0.3 1.1 1.5 1.7 2.3 2.4 2.6 2.9 3.2 4 254.17 52.381 0.3 1.1 1.5 1.7 2.3 2.4 2.6 2.9 3.2 4 255.84 52.217 0.3 1.1 1.5 1.7 2.3 2.4 2.6 2.9 3.2 4 257.5 52.049 0.3 NaN NaN NaN NaN NaN NaN NaN NaN NaN 259.16 51.883 0.3 1 1.4 1.6 2.2 2.3 2.5 2.8 3.1 3.9 260.82 51.711 0.3 1 1.4 1.6 2.2 2.3 2.5 2.8 3.1 3.9 262.48 51.541 0.3 1 1.4 1.6 2.2 2.3 2.5 2.8 3.1 3.9 264.14 51.37 0.3 1 1.4 1.6 2.2 2.3 2.5 2.8 3.1 3.9 265.8 51.201 0.3 1 1.4 1.6 2.2 2.3 2.5 2.8 3.1 3.9 267.46 51.026 0.3 1 1.4 1.6 2.2 2.3 2.5 2.8 3.1 3.9 269.13 50.849 0.3 1 1.4 1.6 2.2 2.3 2.5 2.8 3.1 3.9 270.79 50.671 0.3 1 1.4 1.6 2.2 2.3 2.5 2.8 3.1 3.9 272.45 50.486 0.3 1 1.4 1.6 2.2 2.3 2.5 2.8 3.1 3.9 274.11 50.296 0.3 1 1.4 1.6 2.2 2.3 2.5 2.8 3.1 3.9 275.77 50.09 0.3 1 1.4 1.6 2.2 2.3 2.5 2.8 3.1 3.9 277.43 49.88 0.3 1 1.4 1.6 2.2 2.3 2.5 2.8 3.1 3.9 279.09 49.657 0.3 1 1.4 1.6 2.2 2.3 2.5 2.8 3.1 3.9 280.75 49.426 0.3 1 1.4 1.6 2.2 2.3 2.5 2.8 3.1 NaN 282.42 49.183 0.3 1 1.4 1.6 2.2 2.3 2.5 2.8 3.1 3.8 284.08 48.932 0.3 1 1.4 1.6 2.2 2.3 2.5 2.8 3.1 3.8 285.74 48.68 0.3 1 1.4 1.6 NaN NaN NaN NaN NaN NaN 287.4 48.418 0.3 1 1.4 1.6 2.1 2.2 2.4 2.7 3 3.7 289.06 48.155 0.3 1 1.4 1.6 2.1 2.2 2.4 2.7 3 3.7 290.72 47.888 0.3 1 1.4 1.6 2.1 2.2 2.4 2.7 3 3.7 292.38 47.623 0.3 1 1.4 1.6 2.1 2.2 2.4 2.7 3 3.7 294.04 47.355 0.3 1 1.4 1.6 2.1 2.2 2.4 2.7 3 3.7 295.71 47.082 0.3 1 1.4 1.6 2.1 2.2 2.4 2.7 3 3.7 297.37 46.806 0.3 1 1.4 1.6 2.1 2.2 2.4 2.7 NaN NaN 299.03 46.528 0.3 1 1.4 1.6 2.1 2.2 2.4 2.7 2.9 3.6 300.69 46.25 0.3 1 1.4 1.6 2.1 2.2 2.4 2.7 2.9 3.6 302.35 45.963 0.3 1 1.4 1.6 2.1 2.2 2.4 2.7 2.9 3.6 304.01 45.678 0.3 1 1.4 1.6 2.1 2.2 2.4 2.7 2.9 3.6 305.67 45.386 0.3 1 1.4 1.6 2.1 2.2 2.4 NaN NaN NaN 307.33 45.099 0.3 NaN NaN NaN NaN NaN NaN NaN NaN NaN 309 44.808 0.3 0.9 1.3 1.5 2 2.1 2.3 2.5 2.7 3.4 310.66 44.518 0.3 0.9 1.3 1.5 2 2.1 2.3 2.5 2.7 3.4 312.32 44.23 0.3 0.9 1.3 1.5 2 2.1 2.3 2.5 2.7 3.4 313.98 43.937 0.3 0.9 1.3 1.5 2 2.1 2.3 2.5 2.7 3.4 315.64 43.646 0.3 0.9 1.3 1.5 2 2.1 2.3 2.5 2.7 3.4 317.3 43.354 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 318.96 43.068 0.2 0.8 1.2 1.4 1.9 2 2.2 2.4 2.6 3.3 320.62 42.792 0.2 0.8 NaN NaN NaN NaN NaN NaN NaN NaN 322.29 42.523 0.2 0.8 1.1 1.3 1.8 1.9 2.1 2.3 2.5 3.1 323.95 42.264 0.2 0.8 1.1 1.3 1.8 1.9 2.1 2.3 2.5 3.1 325.61 42.018 0.2 0.8 1.1 1.3 1.8 1.9 2.1 2.3 2.5 3.1 327.27 41.785 0.2 0.8 1.1 1.3 1.8 1.9 2.1 2.3 2.5 3.1 328.93 41.568 0.2 0.8 1.1 1.3 1.8 1.9 2.1 2.3 2.5 3.1 330.59 41.355 0.2 0.8 1.1 1.3 1.8 1.9 2.1 2.3 2.5 3.1 332.25 41.159 0.2 0.8 1.1 1.3 1.8 1.9 2.1 2.3 2.5 3.1 332.25 41.159 0.2 0.8 1.1 1.3 1.8 1.9 2.1 2.3 2.5 3.1
t1_tops = t1;
t1_tops(:,3:end) = varfun(@place_NaNs_at_tops_of_layers,t1,'InputVariables',3:size(t1,2));
disp(t1_tops);
xc gBt z1 z2 z3 z4 z5 z6 z7 z8 z9 z10 ______ ______ ___ ___ ___ ___ ___ ___ ___ ___ ___ ___ 0 60.791 0.4 1.3 1.8 2.1 2.8 2.9 3.2 3.5 3.8 4.7 1.6613 60.558 0.4 1.3 1.8 2.1 2.8 2.9 3.2 3.5 3.8 4.7 3.3225 60.325 0.4 1.3 1.8 2.1 2.8 2.9 3.2 3.5 3.8 4.7 4.9838 60.098 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 6.6451 59.875 0.3 1.2 1.7 NaN NaN NaN NaN NaN NaN NaN 8.3063 59.659 0.3 1.2 1.7 1.9 2.6 2.7 3 3.3 3.6 4.5 9.9676 59.444 0.3 1.2 1.7 1.9 2.6 2.7 3 3.3 3.6 4.5 11.629 59.234 0.3 1.2 1.7 1.9 2.6 2.7 3 3.3 3.6 4.5 13.29 59.031 0.3 1.2 1.7 1.9 2.6 2.7 3 3.3 3.6 4.5 14.951 58.832 0.3 NaN NaN NaN NaN NaN NaN NaN NaN NaN 16.613 58.639 0.3 1.1 1.6 1.8 2.5 2.6 2.9 3.2 3.5 4.4 18.274 58.446 0.3 1.1 1.6 1.8 2.5 2.6 2.9 3.2 3.5 4.4 19.935 58.263 0.3 1.1 1.6 1.8 2.5 2.6 2.9 3.2 3.5 4.4 21.596 58.083 0.3 1.1 1.6 1.8 2.5 2.6 2.9 3.2 3.5 4.4 23.258 57.907 0.3 1.1 1.6 1.8 2.5 2.6 2.9 3.2 3.5 4.4 24.919 57.731 0.3 1.1 1.6 1.8 2.5 2.6 2.9 3.2 3.5 4.4 26.58 57.56 0.3 1.1 1.6 1.8 2.5 2.6 2.9 3.2 3.5 4.4 28.242 57.395 0.3 1.1 1.6 1.8 NaN NaN NaN NaN NaN NaN 29.903 57.231 0.3 1.1 1.6 1.8 2.4 2.5 2.8 3.1 3.4 4.3 31.564 57.068 0.3 1.1 1.6 1.8 2.4 2.5 2.8 3.1 3.4 4.3 33.225 56.906 0.3 1.1 1.6 1.8 2.4 2.5 2.8 3.1 3.4 4.3 34.887 56.753 0.3 1.1 1.6 1.8 2.4 2.5 2.8 3.1 3.4 4.3 36.548 56.602 0.3 1.1 1.6 1.8 2.4 2.5 2.8 3.1 3.4 4.3 38.209 56.45 0.3 1.1 1.6 1.8 2.4 2.5 2.8 3.1 3.4 4.3 39.87 56.301 0.3 1.1 1.6 1.8 2.4 2.5 2.8 3.1 3.4 4.3 41.532 56.156 0.3 1.1 1.6 1.8 2.4 2.5 2.8 3.1 3.4 4.3 43.193 56.017 0.3 1.1 1.6 1.8 2.4 2.5 2.8 3.1 3.4 4.3 44.854 55.874 0.3 1.1 1.6 1.8 2.4 2.5 2.8 3.1 3.4 NaN 46.516 55.737 0.3 1.1 1.6 1.8 2.4 2.5 2.8 3.1 3.4 4.2 48.177 55.602 0.3 1.1 1.6 1.8 2.4 2.5 2.8 3.1 3.4 4.2 49.838 55.471 0.3 1.1 1.6 1.8 2.4 2.5 2.8 3.1 3.4 4.2 51.499 55.339 0.3 1.1 1.6 1.8 2.4 2.5 2.8 3.1 3.4 4.2 53.161 55.209 0.3 1.1 1.6 1.8 2.4 2.5 2.8 3.1 3.4 4.2 54.822 55.082 0.3 1.1 1.6 1.8 2.4 2.5 2.8 3.1 3.4 4.2 56.483 54.956 0.3 1.1 1.6 1.8 2.4 2.5 2.8 3.1 3.4 4.2 58.144 54.833 0.3 1.1 NaN NaN NaN NaN NaN NaN NaN NaN 59.806 54.707 0.3 1.1 1.5 1.7 2.3 2.4 2.7 3 3.3 4.1 61.467 54.587 0.3 1.1 1.5 1.7 2.3 2.4 2.7 3 3.3 4.1 63.128 54.467 0.3 1.1 1.5 1.7 2.3 2.4 2.7 3 3.3 4.1 64.789 54.35 0.3 1.1 1.5 1.7 2.3 2.4 2.7 3 3.3 4.1 66.451 54.233 0.3 1.1 1.5 1.7 2.3 2.4 2.7 3 3.3 4.1 68.112 54.122 0.3 1.1 1.5 1.7 2.3 2.4 NaN NaN NaN NaN 69.773 54.019 0.3 1.1 1.5 1.7 2.3 2.4 2.6 2.9 3.2 4 71.435 53.919 0.3 1.1 1.5 1.7 2.3 2.4 2.6 2.9 3.2 4 73.096 53.829 0.3 1.1 1.5 1.7 2.3 2.4 2.6 2.9 3.2 4 74.757 53.739 0.3 1.1 1.5 1.7 2.3 2.4 2.6 2.9 3.2 4 76.418 53.656 0.3 1.1 1.5 1.7 2.3 2.4 2.6 2.9 3.2 4 78.08 53.575 0.3 1.1 1.5 1.7 2.3 2.4 2.6 2.9 3.2 4 79.741 53.494 0.3 1.1 1.5 1.7 2.3 2.4 2.6 2.9 3.2 4 81.402 53.416 0.3 1.1 1.5 1.7 2.3 2.4 2.6 2.9 3.2 4 83.063 53.341 0.3 1.1 1.5 1.7 2.3 2.4 2.6 2.9 3.2 4 84.725 53.27 0.3 1.1 1.5 1.7 2.3 2.4 2.6 2.9 3.2 4 86.386 53.198 0.3 1.1 1.5 1.7 2.3 2.4 2.6 2.9 3.2 4 88.047 53.128 0.3 1.1 1.5 1.7 2.3 2.4 2.6 2.9 3.2 4 89.708 53.062 0.3 1.1 1.5 1.7 2.3 2.4 2.6 2.9 3.2 4 91.37 52.996 0.3 1.1 1.5 1.7 2.3 2.4 2.6 2.9 3.2 4 93.031 52.931 0.3 1.1 1.5 1.7 2.3 2.4 2.6 2.9 3.2 4 94.692 52.863 0.3 1.1 1.5 1.7 2.3 2.4 2.6 2.9 3.2 4 96.354 52.795 0.3 1.1 1.5 1.7 2.3 2.4 2.6 2.9 3.2 4 98.015 52.727 0.3 1.1 1.5 1.7 2.3 2.4 2.6 2.9 3.2 4 99.676 52.657 0.3 1.1 1.5 1.7 2.3 2.4 2.6 2.9 3.2 4 101.34 52.582 0.3 1.1 1.5 1.7 2.3 2.4 2.6 2.9 3.2 4 103 52.514 0.3 1.1 1.5 1.7 2.3 2.4 2.6 2.9 3.2 4 104.66 52.447 0.3 1.1 1.5 1.7 2.3 2.4 2.6 2.9 3.2 4 106.32 52.389 0.3 1.1 1.5 1.7 2.3 2.4 2.6 2.9 3.2 4 107.98 52.332 0.3 1.1 1.5 1.7 2.3 2.4 2.6 2.9 3.2 4 109.64 52.281 0.3 1.1 1.5 1.7 2.3 2.4 2.6 2.9 3.2 4 111.3 52.237 0.3 1.1 1.5 1.7 2.3 2.4 2.6 2.9 3.2 4 112.97 52.2 0.3 1.1 1.5 1.7 2.3 2.4 2.6 2.9 3.2 4 114.63 52.184 0.3 1.1 1.5 1.7 2.3 2.4 2.6 2.9 3.2 4 116.29 52.172 0.3 1.1 1.5 1.7 2.3 2.4 2.6 2.9 3.2 4 117.95 52.173 0.3 1.1 1.5 1.7 2.3 2.4 2.6 2.9 3.2 4 119.61 52.177 0.3 1.1 1.5 1.7 2.3 2.4 2.6 2.9 3.2 4 121.27 52.188 0.3 1.1 1.5 1.7 2.3 2.4 2.6 2.9 3.2 4 122.93 52.201 0.3 1.1 1.5 1.7 2.3 2.4 2.6 2.9 3.2 4 124.6 52.223 0.3 1.1 1.5 1.7 2.3 2.4 2.6 2.9 3.2 4 126.26 52.257 0.3 1.1 1.5 1.7 2.3 2.4 2.6 2.9 3.2 4 127.92 52.297 0.3 1.1 1.5 1.7 2.3 2.4 2.6 2.9 3.2 4 129.58 52.346 0.3 1.1 1.5 1.7 2.3 2.4 2.6 2.9 3.2 4 131.24 52.397 0.3 1.1 1.5 1.7 2.3 2.4 2.6 2.9 3.2 4 132.9 52.453 0.3 1.1 1.5 1.7 2.3 2.4 2.6 2.9 3.2 4 134.56 52.513 0.3 1.1 1.5 1.7 2.3 2.4 2.6 2.9 3.2 4 136.22 52.574 0.3 1.1 1.5 1.7 2.3 2.4 2.6 2.9 3.2 4 137.89 52.64 0.3 1.1 1.5 1.7 2.3 2.4 2.6 2.9 3.2 4 139.55 52.708 0.3 1.1 1.5 1.7 2.3 2.4 2.6 2.9 3.2 4 141.21 52.779 0.3 1.1 1.5 1.7 2.3 2.4 2.6 2.9 3.2 4 142.87 52.852 0.3 1.1 1.5 1.7 2.3 2.4 2.6 2.9 3.2 4 144.53 52.924 0.3 1.1 1.5 1.7 2.3 2.4 2.6 2.9 3.2 4 146.19 52.999 0.3 1.1 1.5 1.7 2.3 2.4 2.6 2.9 3.2 4 147.85 53.073 0.3 1.1 1.5 1.7 2.3 2.4 2.6 2.9 3.2 4 149.51 53.146 0.3 1.1 1.5 1.7 2.3 2.4 2.6 2.9 3.2 4 151.18 53.217 0.3 1.1 1.5 1.7 2.3 2.4 2.6 2.9 3.2 4 152.84 53.287 0.3 1.1 1.5 1.7 2.3 2.4 2.6 2.9 3.2 4 154.5 53.356 0.3 1.1 1.5 1.7 2.3 2.4 2.6 2.9 3.2 4 156.16 53.42 0.3 1.1 1.5 1.7 2.3 2.4 2.6 2.9 3.2 4 157.82 53.483 0.3 1.1 1.5 1.7 2.3 2.4 2.6 2.9 3.2 4 159.48 53.543 0.3 1.1 1.5 1.7 2.3 2.4 2.6 2.9 3.2 4 161.14 53.605 0.3 1.1 1.5 1.7 2.3 2.4 2.6 2.9 3.2 4 162.8 53.659 0.3 1.1 1.5 1.7 2.3 2.4 2.6 2.9 3.2 4 164.47 53.709 0.3 1.1 1.5 1.7 2.3 2.4 2.6 2.9 3.2 4 166.13 53.748 0.3 1.1 1.5 1.7 2.3 2.4 2.6 2.9 3.2 4 167.79 53.783 0.3 1.1 1.5 1.7 2.3 2.4 2.6 2.9 3.2 4 169.45 53.815 0.3 1.1 1.5 1.7 2.3 2.4 2.6 2.9 3.2 4 171.11 53.841 0.3 1.1 1.5 1.7 2.3 2.4 2.6 2.9 3.2 4 172.77 53.87 0.3 1.1 1.5 1.7 2.3 2.4 2.6 2.9 3.2 4 174.43 53.895 0.3 1.1 1.5 1.7 2.3 2.4 2.6 2.9 3.2 4 176.09 53.924 0.3 1.1 1.5 1.7 2.3 2.4 2.6 2.9 3.2 4 177.76 53.95 0.3 1.1 1.5 1.7 2.3 2.4 2.6 2.9 3.2 4 179.42 53.978 0.3 1.1 1.5 1.7 2.3 2.4 2.6 2.9 3.2 4 181.08 54.008 0.3 1.1 1.5 1.7 2.3 2.4 2.6 2.9 3.2 4 182.74 54.041 0.3 1.1 1.5 1.7 2.3 2.4 2.6 2.9 3.2 4 184.4 54.075 0.3 1.1 1.5 1.7 2.3 2.4 2.6 2.9 3.2 4 186.06 54.108 0.3 1.1 1.5 1.7 2.3 2.4 2.6 2.9 3.2 4 187.72 54.143 0.3 1.1 1.5 1.7 2.3 2.4 2.6 2.9 3.2 4 189.38 54.176 0.3 1.1 1.5 1.7 2.3 2.4 NaN NaN NaN NaN 191.05 54.209 0.3 1.1 1.5 1.7 2.3 2.4 2.7 3 3.3 4.1 192.71 54.241 0.3 1.1 1.5 1.7 2.3 2.4 2.7 3 3.3 4.1 194.37 54.272 0.3 1.1 1.5 1.7 2.3 2.4 2.7 3 3.3 4.1 196.03 54.3 0.3 1.1 1.5 1.7 2.3 2.4 2.7 3 3.3 4.1 197.69 54.325 0.3 1.1 1.5 1.7 2.3 2.4 2.7 3 3.3 4.1 199.35 54.348 0.3 1.1 1.5 1.7 2.3 2.4 2.7 3 3.3 4.1 201.01 54.366 0.3 1.1 1.5 1.7 2.3 2.4 2.7 3 3.3 4.1 202.67 54.384 0.3 1.1 1.5 1.7 2.3 2.4 2.7 3 3.3 4.1 204.34 54.397 0.3 1.1 1.5 1.7 2.3 2.4 2.7 3 3.3 4.1 206 54.407 0.3 1.1 1.5 1.7 2.3 2.4 2.7 3 3.3 4.1 207.66 54.413 0.3 1.1 1.5 1.7 2.3 2.4 2.7 3 3.3 4.1 209.32 54.419 0.3 1.1 1.5 1.7 2.3 2.4 2.7 3 3.3 4.1 210.98 54.421 0.3 1.1 1.5 1.7 2.3 2.4 2.7 3 3.3 4.1 212.64 54.417 0.3 1.1 1.5 1.7 2.3 2.4 2.7 3 3.3 4.1 214.3 54.412 0.3 1.1 1.5 1.7 2.3 2.4 2.7 3 3.3 4.1 215.96 54.404 0.3 1.1 1.5 1.7 2.3 2.4 2.7 3 3.3 4.1 217.63 54.396 0.3 1.1 1.5 1.7 2.3 2.4 2.7 3 3.3 4.1 219.29 54.378 0.3 1.1 1.5 1.7 2.3 2.4 2.7 3 3.3 4.1 220.95 54.36 0.3 1.1 1.5 1.7 2.3 2.4 2.7 3 3.3 4.1 222.61 54.334 0.3 1.1 1.5 1.7 2.3 2.4 2.7 3 3.3 4.1 224.27 54.302 0.3 1.1 1.5 1.7 2.3 2.4 2.7 3 3.3 4.1 225.93 54.263 0.3 1.1 1.5 1.7 2.3 2.4 2.7 3 3.3 4.1 227.59 54.217 0.3 1.1 1.5 1.7 2.3 2.4 2.7 3 3.3 4.1 229.25 54.168 0.3 1.1 1.5 1.7 2.3 2.4 2.7 3 3.3 4.1 230.92 54.111 0.3 1.1 1.5 1.7 2.3 2.4 NaN NaN NaN NaN 232.58 54.046 0.3 1.1 1.5 1.7 2.3 2.4 2.6 2.9 3.2 4 234.24 53.967 0.3 1.1 1.5 1.7 2.3 2.4 2.6 2.9 3.2 4 235.9 53.884 0.3 1.1 1.5 1.7 2.3 2.4 2.6 2.9 3.2 4 237.56 53.784 0.3 1.1 1.5 1.7 2.3 2.4 2.6 2.9 3.2 4 239.22 53.672 0.3 1.1 1.5 1.7 2.3 2.4 2.6 2.9 3.2 4 240.88 53.552 0.3 1.1 1.5 1.7 2.3 2.4 2.6 2.9 3.2 4 242.55 53.423 0.3 1.1 1.5 1.7 2.3 2.4 2.6 2.9 3.2 4 244.21 53.294 0.3 1.1 1.5 1.7 2.3 2.4 2.6 2.9 3.2 4 245.87 53.153 0.3 1.1 1.5 1.7 2.3 2.4 2.6 2.9 3.2 4 247.53 53.01 0.3 1.1 1.5 1.7 2.3 2.4 2.6 2.9 3.2 4 249.19 52.858 0.3 1.1 1.5 1.7 2.3 2.4 2.6 2.9 3.2 4 250.85 52.704 0.3 1.1 1.5 1.7 2.3 2.4 2.6 2.9 3.2 4 252.51 52.545 0.3 1.1 1.5 1.7 2.3 2.4 2.6 2.9 3.2 4 254.17 52.381 0.3 1.1 1.5 1.7 2.3 2.4 2.6 2.9 3.2 4 255.84 52.217 0.3 1.1 1.5 1.7 2.3 2.4 2.6 2.9 3.2 4 257.5 52.049 0.3 1.1 1.5 1.7 2.3 2.4 2.6 2.9 3.2 4 259.16 51.883 0.3 NaN NaN NaN NaN NaN NaN NaN NaN NaN 260.82 51.711 0.3 1 1.4 1.6 2.2 2.3 2.5 2.8 3.1 3.9 262.48 51.541 0.3 1 1.4 1.6 2.2 2.3 2.5 2.8 3.1 3.9 264.14 51.37 0.3 1 1.4 1.6 2.2 2.3 2.5 2.8 3.1 3.9 265.8 51.201 0.3 1 1.4 1.6 2.2 2.3 2.5 2.8 3.1 3.9 267.46 51.026 0.3 1 1.4 1.6 2.2 2.3 2.5 2.8 3.1 3.9 269.13 50.849 0.3 1 1.4 1.6 2.2 2.3 2.5 2.8 3.1 3.9 270.79 50.671 0.3 1 1.4 1.6 2.2 2.3 2.5 2.8 3.1 3.9 272.45 50.486 0.3 1 1.4 1.6 2.2 2.3 2.5 2.8 3.1 3.9 274.11 50.296 0.3 1 1.4 1.6 2.2 2.3 2.5 2.8 3.1 3.9 275.77 50.09 0.3 1 1.4 1.6 2.2 2.3 2.5 2.8 3.1 3.9 277.43 49.88 0.3 1 1.4 1.6 2.2 2.3 2.5 2.8 3.1 3.9 279.09 49.657 0.3 1 1.4 1.6 2.2 2.3 2.5 2.8 3.1 3.9 280.75 49.426 0.3 1 1.4 1.6 2.2 2.3 2.5 2.8 3.1 3.9 282.42 49.183 0.3 1 1.4 1.6 2.2 2.3 2.5 2.8 3.1 NaN 284.08 48.932 0.3 1 1.4 1.6 2.2 2.3 2.5 2.8 3.1 3.8 285.74 48.68 0.3 1 1.4 1.6 2.2 2.3 2.5 2.8 3.1 3.8 287.4 48.418 0.3 1 1.4 1.6 NaN NaN NaN NaN NaN NaN 289.06 48.155 0.3 1 1.4 1.6 2.1 2.2 2.4 2.7 3 3.7 290.72 47.888 0.3 1 1.4 1.6 2.1 2.2 2.4 2.7 3 3.7 292.38 47.623 0.3 1 1.4 1.6 2.1 2.2 2.4 2.7 3 3.7 294.04 47.355 0.3 1 1.4 1.6 2.1 2.2 2.4 2.7 3 3.7 295.71 47.082 0.3 1 1.4 1.6 2.1 2.2 2.4 2.7 3 3.7 297.37 46.806 0.3 1 1.4 1.6 2.1 2.2 2.4 2.7 3 3.7 299.03 46.528 0.3 1 1.4 1.6 2.1 2.2 2.4 2.7 NaN NaN 300.69 46.25 0.3 1 1.4 1.6 2.1 2.2 2.4 2.7 2.9 3.6 302.35 45.963 0.3 1 1.4 1.6 2.1 2.2 2.4 2.7 2.9 3.6 304.01 45.678 0.3 1 1.4 1.6 2.1 2.2 2.4 2.7 2.9 3.6 305.67 45.386 0.3 1 1.4 1.6 2.1 2.2 2.4 2.7 2.9 3.6 307.33 45.099 0.3 1 1.4 1.6 2.1 2.2 2.4 NaN NaN NaN 309 44.808 0.3 NaN NaN NaN NaN NaN NaN NaN NaN NaN 310.66 44.518 0.3 0.9 1.3 1.5 2 2.1 2.3 2.5 2.7 3.4 312.32 44.23 0.3 0.9 1.3 1.5 2 2.1 2.3 2.5 2.7 3.4 313.98 43.937 0.3 0.9 1.3 1.5 2 2.1 2.3 2.5 2.7 3.4 315.64 43.646 0.3 0.9 1.3 1.5 2 2.1 2.3 2.5 2.7 3.4 317.3 43.354 0.3 0.9 1.3 1.5 2 2.1 2.3 2.5 2.7 3.4 318.96 43.068 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 320.62 42.792 0.2 0.8 1.2 1.4 1.9 2 2.2 2.4 2.6 3.3 322.29 42.523 0.2 0.8 NaN NaN NaN NaN NaN NaN NaN NaN 323.95 42.264 0.2 0.8 1.1 1.3 1.8 1.9 2.1 2.3 2.5 3.1 325.61 42.018 0.2 0.8 1.1 1.3 1.8 1.9 2.1 2.3 2.5 3.1 327.27 41.785 0.2 0.8 1.1 1.3 1.8 1.9 2.1 2.3 2.5 3.1 328.93 41.568 0.2 0.8 1.1 1.3 1.8 1.9 2.1 2.3 2.5 3.1 330.59 41.355 0.2 0.8 1.1 1.3 1.8 1.9 2.1 2.3 2.5 3.1 332.25 41.159 0.2 0.8 1.1 1.3 1.8 1.9 2.1 2.3 2.5 3.1 332.25 41.159 0.2 0.8 1.1 1.3 1.8 1.9 2.1 2.3 2.5 3.1
function x = place_NaNs_at_bottoms_of_layers(x)
x(logical(diff(x))) = NaN;
end
function x = place_NaNs_at_tops_of_layers(x)
x(1+find(diff(x))) = NaN;
end
  15 件のコメント
Moustafa Abedel Fattah
Moustafa Abedel Fattah 2023 年 2 月 13 日
編集済み: Moustafa Abedel Fattah 2023 年 2 月 13 日
Dear Sir;
Excellent efforts god blesse you. Now, please tell me how can I write your formal name as a reference when I finsh and publish my articles (It is very important for refering to your help)
Best Regards;
M.Dahab
Voss
Voss 2023 年 2 月 13 日
You're welcome! You can refer to me as "B. Voss". Thanks!

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その他の回答 (1 件)

Cris LaPierre
Cris LaPierre 2023 年 2 月 9 日
I would use readtable with the 'Range' name-value pair to extract each table separately. readtable will automatically fill any missing data.
file = 'https://www.mathworks.com/matlabcentral/answers/uploaded_files/1290875/data.xlsx';
% A-L
tbl1 = readtable(file,'Range',"A1:L203")
tbl1 = 202×12 table
xc gBt z1 z2 z3 z4 z5 z6 z7 z8 z9 z10 ______ ______ ___ ___ ___ ___ ___ ___ ___ ___ ___ ___ 0 60.791 0.4 1.3 1.8 2.1 2.8 2.9 3.2 3.5 3.8 4.7 1.6613 60.558 0.4 1.3 1.8 2.1 2.8 2.9 3.2 3.5 3.8 4.7 3.3225 60.325 0.4 1.3 1.8 2.1 2.8 2.9 3.2 3.5 3.8 4.7 4.9838 60.098 0.3 1.2 1.7 2 2.7 2.8 3.1 3.4 3.7 4.6 6.6451 59.875 0.3 1.2 1.7 1.9 2.6 2.7 3 3.3 3.6 4.5 8.3063 59.659 0.3 1.2 1.7 1.9 2.6 2.7 3 3.3 3.6 4.5 9.9676 59.444 0.3 1.2 1.7 1.9 2.6 2.7 3 3.3 3.6 4.5 11.629 59.234 0.3 1.2 1.7 1.9 2.6 2.7 3 3.3 3.6 4.5 13.29 59.031 0.3 1.2 1.7 1.9 2.6 2.7 3 3.3 3.6 4.5 14.951 58.832 0.3 1.1 1.6 1.8 2.5 2.6 2.9 3.2 3.5 4.4 16.613 58.639 0.3 1.1 1.6 1.8 2.5 2.6 2.9 3.2 3.5 4.4 18.274 58.446 0.3 1.1 1.6 1.8 2.5 2.6 2.9 3.2 3.5 4.4 19.935 58.263 0.3 1.1 1.6 1.8 2.5 2.6 2.9 3.2 3.5 4.4 21.596 58.083 0.3 1.1 1.6 1.8 2.5 2.6 2.9 3.2 3.5 4.4 23.258 57.907 0.3 1.1 1.6 1.8 2.5 2.6 2.9 3.2 3.5 4.4 24.919 57.731 0.3 1.1 1.6 1.8 2.5 2.6 2.9 3.2 3.5 4.4
% AG-AR
tbl2 = readtable(file,'Range',"AG1:AR203")
tbl2 = 202×12 table
xc gBt z1 z2 z3 z4 z5 z6 z7 z8 z9 z10 ______ ______ ___ ___ ___ ___ ___ ___ ___ ___ ___ ___ 0 60.791 0.4 1.3 1.8 2.1 2.8 2.9 3.2 3.5 3.8 4.7 1.6613 60.558 0.4 1.3 1.8 2.1 2.8 2.9 3.2 3.5 3.8 4.7 3.3225 60.325 NaN 1.3 NaN NaN NaN NaN NaN NaN NaN NaN 4.9838 60.098 0.3 NaN 1.7 NaN NaN NaN NaN NaN NaN NaN 6.6451 59.875 0.3 1.2 1.7 1.9 2.6 2.7 3 3.3 3.6 4.5 8.3063 59.659 0.3 1.2 1.7 1.9 2.6 2.7 3 3.3 3.6 4.5 9.9676 59.444 0.3 1.2 1.7 1.9 2.6 2.7 3 3.3 3.6 4.5 11.629 59.234 0.3 1.2 1.7 1.9 2.6 2.7 3 3.3 3.6 4.5 13.29 59.031 0.3 NaN NaN NaN NaN NaN NaN NaN NaN NaN 14.951 58.832 0.3 1.1 1.6 1.8 2.5 2.6 2.9 3.2 3.5 4.4 16.613 58.639 0.3 1.1 1.6 1.8 2.5 2.6 2.9 3.2 3.5 4.4 18.274 58.446 0.3 1.1 1.6 1.8 2.5 2.6 2.9 3.2 3.5 4.4 19.935 58.263 0.3 1.1 1.6 1.8 2.5 2.6 2.9 3.2 3.5 4.4 21.596 58.083 0.3 1.1 1.6 1.8 2.5 2.6 2.9 3.2 3.5 4.4 23.258 57.907 0.3 1.1 1.6 1.8 2.5 2.6 2.9 3.2 3.5 4.4 24.919 57.731 0.3 1.1 1.6 1.8 2.5 2.6 2.9 3.2 3.5 4.4

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