How do I create a 3D plot using the messgrid command from data within an Excel file?

12 Jul. 2023
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Aktualisiert 14 Jul. 2023
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I need help creating a 3D plot using data from an excel file. I went to have the three axis be temperature, frequency and Epsilon values. The first row of the excel file is the temperature starting from 160 to 60 in Celsius. The first column is the frequency in Hertz, while the values in the middle are the epsilon values for a specifc temperature and frequency.
Here is what I have so far. My main issue is that I am getting error while using the meshgrid command. To be specific I am getting the following error:
Invalid expression. When calling a function or indexing a variable, use parentheses. Otherwise, check for mismatched delimiters.
data_epp=readtable('Epsilon_Prime.xlsx'); % reads data
x=data_epp(1,3:end); % temperature(C)
y=data_epp(2:end,1); % freqeuncy(Hz)
z=data_epp(:,3:end); % epsilon prime
[X,Y}=meshgrid(x,y);
mesh(X,Y,z)

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Antworten (2)

Voss
Voss am 12 Jul. 2023
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data_epp=readtable('Epsilon_Prime.xlsx'); % reads data
x=data_epp{1,3:end}; % temperature(C)
y=data_epp{2:end,1}; % freqeuncy(Hz)
z=data_epp{2:end,3:end}; % epsilon prime
[X,Y]=meshgrid(x,y);
mesh(X,Y,z)
Star Strider
Star Strider am 13 Jul. 2023
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Ran in:
The meshgrid call is actually not necessary.
Try this —
T1 = readtable('Epsilon_Prime.xlsx')
T1 = 96×52 table
Var1 Var2 Var3 Var4 Var5 Var6 Var7 Var8 Var9 Var10 Var11 Var12 Var13 Var14 Var15 Var16 Var17 Var18 Var19 Var20 Var21 Var22 Var23 Var24 Var25 Var26 Var27 Var28 Var29 Var30 Var31 Var32 Var33 Var34 Var35 Var36 Var37 Var38 Var39 Var40 Var41 Var42 Var43 Var44 Var45 Var46 Var47 Var48 Var49 Var50 Var51 Var52 ______ ____________ ______ ______ ______ ______ ______ ______ ______ ______ ______ ______ ______ ______ ______ ______ ______ ______ ______ ______ ______ ______ ______ ______ ______ ______ ______ ______ ______ ______ ______ ______ ______ ______ ______ ______ ______ ______ ______ ______ ______ _______ _______ _______ _______ _______ _______ _______ _______ _______ _______ _______ NaN {'160' } 158 156 154 152 150 148 146 144 142 140 138 136 134 132 130 128 126 124 122 120 118 116 114 112 110 108 106 104 102 100 98 96 94 92 90 88 86 84 82 80 78 76 74 72 70 68 66 64 62 60 20 {'nan' } NaN 5376.5 4420.5 3450.3 3299.9 2746.2 2471.8 2221.8 2112.4 2042.9 2012.1 1894.2 1929.5 1988.6 2044.6 2117.8 2187.4 2272.9 2364.9 2493.6 2672.2 2827.1 3045.7 3291.3 3618.7 3987.5 4468 5046.2 5737.1 6417.5 7385.6 NaN 9257.9 10261 11192 12226 13050 13758 14261 14243 13811 14021 13978 13440 13356 13123 13573 12693 12744 12161 22.44 {'17.52692'} 16.682 16.229 15.513 14.814 17.273 16.247 14.613 13.45 12.377 11.049 7.5733 6.9713 6.4226 5.9996 5.5385 5.2075 4.8715 4.6357 4.4398 4.2745 4.226 4.1981 4.1285 4.1565 4.1872 4.2628 4.2707 4.3972 4.4723 4.4926 4.5755 4.5357 4.4866 4.5057 4.3043 3.9741 3.4877 3.2215 2.8953 2.4634 2.1093 1.8325 1.6805 1.4388 1.2973 1.0391 0.9618 0.81599 0.72641 0.61435 25.179 {'nan' } 16.257 15.747 15.123 14.39 16.852 15.931 14.24 13.078 11.998 10.775 7.2923 6.7532 6.2304 5.8096 5.4412 5.1063 4.8377 4.5811 4.4363 4.2889 4.2585 4.2283 4.2084 4.2252 4.2628 4.3197 4.4501 4.5162 4.5737 4.6372 4.6715 4.443 4.3951 4.2685 3.8344 3.7848 3.3584 2.9628 2.5779 2.3207 1.9965 1.7474 1.5044 1.2989 1.1261 0.98147 0.85201 0.76012 0.66784 0.59448 28.251 {'nan' } 15.777 15.3 14.666 13.93 16.513 15.584 13.842 12.755 11.674 10.482 7.0428 6.5424 6.0642 5.6878 5.3285 5.0511 4.7949 4.5743 4.4362 4.321 4.3006 4.3014 4.2889 4.3286 4.3658 4.4403 4.5339 4.6716 4.6486 4.6768 4.7136 4.4015 4.3075 4.1149 3.757 3.6069 3.2197 2.7823 2.428 2.1371 1.8373 1.5828 1.4141 1.1906 1.016 0.90325 0.79465 0.71238 0.61572 0.55023 31.698 {'nan' } 15.262 14.82 14.207 13.436 16.177 15.233 13.548 12.438 11.376 10.177 6.849 6.3689 5.9209 5.5953 5.2592 4.9978 4.7786 4.5871 4.464 4.3537 4.3684 4.3808 4.4017 4.4507 4.4953 4.6389 4.6763 4.7346 4.7377 4.7729 4.6833 4.3715 4.2334 3.9305 3.5475 3.2924 2.9309 2.6171 2.2435 1.9859 1.6591 1.4614 1.2674 1.0903 0.95361 0.85184 0.74612 0.65334 0.58556 0.51652 35.566 {'nan' } 14.735 14.359 13.684 12.949 15.835 14.935 13.253 12.102 11.101 9.9636 6.6529 6.2191 5.8046 5.4979 5.2067 4.9945 4.7946 4.5709 4.4927 4.424 4.4442 4.4892 4.4928 4.5621 4.6094 4.7469 4.8516 4.7819 4.7527 4.7858 4.5932 4.2358 4.017 3.7528 3.494 3.0737 2.771 2.4239 2.0717 1.824 1.5836 1.3509 1.2035 1.0397 0.87759 0.78836 0.70779 0.61874 0.56684 0.49003 39.905 {'nan' } 14.172 13.767 13.277 12.486 15.443 14.565 12.909 11.856 10.81 9.7475 6.489 6.1072 5.7281 5.4798 5.1792 4.9818 4.8607 4.6284 4.5973 4.4787 4.493 4.5232 4.6659 4.7873 4.6949 4.7636 5.0295 4.774 4.8862 4.8209 4.4322 4.3568 3.7905 3.7466 3.0341 2.9156 2.4104 2.3544 1.9128 1.6361 1.4047 1.2034 1.1589 1.0076 0.87016 0.69415 0.65249 0.54458 0.53011 0.48351 44.774 {'nan' } 13.578 13.224 12.649 11.937 15.279 14.236 12.706 11.559 10.635 9.5921 6.3488 6.0191 5.6809 5.4067 5.1316 4.9822 4.8168 4.6503 4.6602 4.6386 4.6118 4.6488 4.7766 4.8578 4.724 5.1356 4.8483 4.9241 4.9154 4.3778 4.4654 4.0472 3.7151 3.3326 2.7393 2.6422 2.2453 2.1551 1.8712 1.466 1.202 1.0824 0.96656 0.88107 0.70471 0.76874 0.48341 0.43151 0.48964 0.30726 50.238 {'nan' } 13.181 12.657 12.035 11.307 14.747 13.922 12.411 11.294 10.538 9.3844 6.3283 5.9895 5.5952 5.4603 5.1404 5.1201 4.9845 4.774 4.8837 4.6371 4.6775 4.7317 4.8217 4.7839 4.7557 4.6974 5 5.4211 5.1567 4.8857 4.3749 3.4022 3.4062 3.0238 2.5556 2.2236 1.8802 2.2239 1.6354 1.3123 1.4197 1.2952 1.0333 0.6733 0.70021 0.66624 0.47653 0.4324 0.40965 0.37803 56.368 {'nan' } 12.453 12.199 11.631 10.871 14.611 13.731 12.119 11.122 10.197 9.1997 6.1608 5.8724 5.5744 5.3878 5.2531 5.1002 4.9818 4.8562 4.8556 4.8613 4.9171 5.0178 5.0621 5.1299 5.1227 5.1052 5.1229 4.7985 4.7768 4.476 4.025 3.5122 3.1307 3.0766 2.6014 2.1701 1.9231 1.8632 1.4606 1.2401 1.2209 1.036 0.82349 0.69554 0.84908 0.49442 0.36409 0.58977 0.59532 0.53822 63.246 {'nan' } 11.85 11.646 11.101 10.361 14.354 13.454 11.91 10.956 10.106 9.1185 6.1133 5.8441 5.5997 5.4702 5.29 5.2306 5.1451 5.008 4.9988 4.9739 5.1234 5.2222 5.1696 5.222 5.1467 5.177 5.1029 4.8543 4.4666 4.1844 3.9322 3.4455 3.1073 2.675 2.3884 2.1701 1.8817 1.6177 1.3643 1.2149 1.097 0.8983 0.75204 0.7112 0.59941 0.49777 0.50007 0.43015 0.35473 0.35647 70.963 {'nan' } 11.272 11.131 10.563 9.8645 14.017 13.251 11.773 10.907 9.9259 8.9618 6.0241 5.8335 5.597 5.4689 5.4775 5.2462 5.1924 5.1808 5.2764 5.2629 5.3448 5.2989 5.5159 5.542 5.1946 5.0397 4.9717 4.9653 4.663 4.1506 3.8881 3.1409 2.7407 2.7692 2.3558 1.8736 1.6288 1.4285 1.3477 1.0994 1.0693 0.9129 0.83627 0.66831 0.60707 0.45439 0.48332 0.4231 0.3083 0.2997 79.621 {'nan' } 10.7 10.54 10.099 9.3385 13.881 13.035 11.57 10.69 9.9195 9.0222 6.1052 5.8896 5.707 5.6338 5.5266 5.4632 5.4244 5.3443 5.3829 5.2937 5.3879 5.5386 5.4791 5.3686 5.3063 5.0993 4.8638 4.6757 4.2322 3.8743 3.4545 2.9151 2.6263 2.4567 2.0297 1.9898 1.6203 1.4012 1.2671 0.95795 1.0546 0.70973 0.85721 0.55748 0.62035 0.4175 0.48917 0.27234 0.24339 0.45411 89.337 {'nan' } 10.144 10.013 9.5853 8.8827 13.734 12.896 11.457 10.634 9.8922 9.0911 6.1461 5.9725 5.8421 5.771 5.6901 5.6415 5.6119 5.5235 5.5234 5.5077 5.6006 5.6284 5.5289 5.3919 5.2247 4.9932 4.7759 4.4167 4.0578 3.6918 3.3276 2.8467 2.5292 2.2461 2.0057 1.745 1.5326 1.3179 1.1513 1.028 0.90093 0.77813 0.68866 0.60966 0.54931 0.48243 0.43547 0.38952 0.34448 0.31193 100.24 {'nan' } 9.6035 9.5045 9.095 8.457 13.586 12.798 11.383 10.628 9.9318 9.1948 6.2465 6.1046 5.9906 5.9624 5.8924 5.8851 5.838 5.7496 5.743 5.6688 5.7657 5.7238 5.6154 5.4315 5.1992 4.9551 4.7044 4.273 3.8839 3.5011 3.1395 2.6889 2.3813 2.1328 1.8586 1.6161 1.433 1.2634 1.084 0.96689 0.86359 0.72942 0.66981 0.56846 0.51631 0.45716 0.41264 0.3716 0.35238 0.29838
T1.Var2 = str2double(T1.Var2)
T1 = 96×52 table
Var1 Var2 Var3 Var4 Var5 Var6 Var7 Var8 Var9 Var10 Var11 Var12 Var13 Var14 Var15 Var16 Var17 Var18 Var19 Var20 Var21 Var22 Var23 Var24 Var25 Var26 Var27 Var28 Var29 Var30 Var31 Var32 Var33 Var34 Var35 Var36 Var37 Var38 Var39 Var40 Var41 Var42 Var43 Var44 Var45 Var46 Var47 Var48 Var49 Var50 Var51 Var52 ______ ______ ______ ______ ______ ______ ______ ______ ______ ______ ______ ______ ______ ______ ______ ______ ______ ______ ______ ______ ______ ______ ______ ______ ______ ______ ______ ______ ______ ______ ______ ______ ______ ______ ______ ______ ______ ______ ______ ______ ______ _______ _______ _______ _______ _______ _______ _______ _______ _______ _______ _______ NaN 160 158 156 154 152 150 148 146 144 142 140 138 136 134 132 130 128 126 124 122 120 118 116 114 112 110 108 106 104 102 100 98 96 94 92 90 88 86 84 82 80 78 76 74 72 70 68 66 64 62 60 20 NaN NaN 5376.5 4420.5 3450.3 3299.9 2746.2 2471.8 2221.8 2112.4 2042.9 2012.1 1894.2 1929.5 1988.6 2044.6 2117.8 2187.4 2272.9 2364.9 2493.6 2672.2 2827.1 3045.7 3291.3 3618.7 3987.5 4468 5046.2 5737.1 6417.5 7385.6 NaN 9257.9 10261 11192 12226 13050 13758 14261 14243 13811 14021 13978 13440 13356 13123 13573 12693 12744 12161 22.44 17.527 16.682 16.229 15.513 14.814 17.273 16.247 14.613 13.45 12.377 11.049 7.5733 6.9713 6.4226 5.9996 5.5385 5.2075 4.8715 4.6357 4.4398 4.2745 4.226 4.1981 4.1285 4.1565 4.1872 4.2628 4.2707 4.3972 4.4723 4.4926 4.5755 4.5357 4.4866 4.5057 4.3043 3.9741 3.4877 3.2215 2.8953 2.4634 2.1093 1.8325 1.6805 1.4388 1.2973 1.0391 0.9618 0.81599 0.72641 0.61435 25.179 NaN 16.257 15.747 15.123 14.39 16.852 15.931 14.24 13.078 11.998 10.775 7.2923 6.7532 6.2304 5.8096 5.4412 5.1063 4.8377 4.5811 4.4363 4.2889 4.2585 4.2283 4.2084 4.2252 4.2628 4.3197 4.4501 4.5162 4.5737 4.6372 4.6715 4.443 4.3951 4.2685 3.8344 3.7848 3.3584 2.9628 2.5779 2.3207 1.9965 1.7474 1.5044 1.2989 1.1261 0.98147 0.85201 0.76012 0.66784 0.59448 28.251 NaN 15.777 15.3 14.666 13.93 16.513 15.584 13.842 12.755 11.674 10.482 7.0428 6.5424 6.0642 5.6878 5.3285 5.0511 4.7949 4.5743 4.4362 4.321 4.3006 4.3014 4.2889 4.3286 4.3658 4.4403 4.5339 4.6716 4.6486 4.6768 4.7136 4.4015 4.3075 4.1149 3.757 3.6069 3.2197 2.7823 2.428 2.1371 1.8373 1.5828 1.4141 1.1906 1.016 0.90325 0.79465 0.71238 0.61572 0.55023 31.698 NaN 15.262 14.82 14.207 13.436 16.177 15.233 13.548 12.438 11.376 10.177 6.849 6.3689 5.9209 5.5953 5.2592 4.9978 4.7786 4.5871 4.464 4.3537 4.3684 4.3808 4.4017 4.4507 4.4953 4.6389 4.6763 4.7346 4.7377 4.7729 4.6833 4.3715 4.2334 3.9305 3.5475 3.2924 2.9309 2.6171 2.2435 1.9859 1.6591 1.4614 1.2674 1.0903 0.95361 0.85184 0.74612 0.65334 0.58556 0.51652 35.566 NaN 14.735 14.359 13.684 12.949 15.835 14.935 13.253 12.102 11.101 9.9636 6.6529 6.2191 5.8046 5.4979 5.2067 4.9945 4.7946 4.5709 4.4927 4.424 4.4442 4.4892 4.4928 4.5621 4.6094 4.7469 4.8516 4.7819 4.7527 4.7858 4.5932 4.2358 4.017 3.7528 3.494 3.0737 2.771 2.4239 2.0717 1.824 1.5836 1.3509 1.2035 1.0397 0.87759 0.78836 0.70779 0.61874 0.56684 0.49003 39.905 NaN 14.172 13.767 13.277 12.486 15.443 14.565 12.909 11.856 10.81 9.7475 6.489 6.1072 5.7281 5.4798 5.1792 4.9818 4.8607 4.6284 4.5973 4.4787 4.493 4.5232 4.6659 4.7873 4.6949 4.7636 5.0295 4.774 4.8862 4.8209 4.4322 4.3568 3.7905 3.7466 3.0341 2.9156 2.4104 2.3544 1.9128 1.6361 1.4047 1.2034 1.1589 1.0076 0.87016 0.69415 0.65249 0.54458 0.53011 0.48351 44.774 NaN 13.578 13.224 12.649 11.937 15.279 14.236 12.706 11.559 10.635 9.5921 6.3488 6.0191 5.6809 5.4067 5.1316 4.9822 4.8168 4.6503 4.6602 4.6386 4.6118 4.6488 4.7766 4.8578 4.724 5.1356 4.8483 4.9241 4.9154 4.3778 4.4654 4.0472 3.7151 3.3326 2.7393 2.6422 2.2453 2.1551 1.8712 1.466 1.202 1.0824 0.96656 0.88107 0.70471 0.76874 0.48341 0.43151 0.48964 0.30726 50.238 NaN 13.181 12.657 12.035 11.307 14.747 13.922 12.411 11.294 10.538 9.3844 6.3283 5.9895 5.5952 5.4603 5.1404 5.1201 4.9845 4.774 4.8837 4.6371 4.6775 4.7317 4.8217 4.7839 4.7557 4.6974 5 5.4211 5.1567 4.8857 4.3749 3.4022 3.4062 3.0238 2.5556 2.2236 1.8802 2.2239 1.6354 1.3123 1.4197 1.2952 1.0333 0.6733 0.70021 0.66624 0.47653 0.4324 0.40965 0.37803 56.368 NaN 12.453 12.199 11.631 10.871 14.611 13.731 12.119 11.122 10.197 9.1997 6.1608 5.8724 5.5744 5.3878 5.2531 5.1002 4.9818 4.8562 4.8556 4.8613 4.9171 5.0178 5.0621 5.1299 5.1227 5.1052 5.1229 4.7985 4.7768 4.476 4.025 3.5122 3.1307 3.0766 2.6014 2.1701 1.9231 1.8632 1.4606 1.2401 1.2209 1.036 0.82349 0.69554 0.84908 0.49442 0.36409 0.58977 0.59532 0.53822 63.246 NaN 11.85 11.646 11.101 10.361 14.354 13.454 11.91 10.956 10.106 9.1185 6.1133 5.8441 5.5997 5.4702 5.29 5.2306 5.1451 5.008 4.9988 4.9739 5.1234 5.2222 5.1696 5.222 5.1467 5.177 5.1029 4.8543 4.4666 4.1844 3.9322 3.4455 3.1073 2.675 2.3884 2.1701 1.8817 1.6177 1.3643 1.2149 1.097 0.8983 0.75204 0.7112 0.59941 0.49777 0.50007 0.43015 0.35473 0.35647 70.963 NaN 11.272 11.131 10.563 9.8645 14.017 13.251 11.773 10.907 9.9259 8.9618 6.0241 5.8335 5.597 5.4689 5.4775 5.2462 5.1924 5.1808 5.2764 5.2629 5.3448 5.2989 5.5159 5.542 5.1946 5.0397 4.9717 4.9653 4.663 4.1506 3.8881 3.1409 2.7407 2.7692 2.3558 1.8736 1.6288 1.4285 1.3477 1.0994 1.0693 0.9129 0.83627 0.66831 0.60707 0.45439 0.48332 0.4231 0.3083 0.2997 79.621 NaN 10.7 10.54 10.099 9.3385 13.881 13.035 11.57 10.69 9.9195 9.0222 6.1052 5.8896 5.707 5.6338 5.5266 5.4632 5.4244 5.3443 5.3829 5.2937 5.3879 5.5386 5.4791 5.3686 5.3063 5.0993 4.8638 4.6757 4.2322 3.8743 3.4545 2.9151 2.6263 2.4567 2.0297 1.9898 1.6203 1.4012 1.2671 0.95795 1.0546 0.70973 0.85721 0.55748 0.62035 0.4175 0.48917 0.27234 0.24339 0.45411 89.337 NaN 10.144 10.013 9.5853 8.8827 13.734 12.896 11.457 10.634 9.8922 9.0911 6.1461 5.9725 5.8421 5.771 5.6901 5.6415 5.6119 5.5235 5.5234 5.5077 5.6006 5.6284 5.5289 5.3919 5.2247 4.9932 4.7759 4.4167 4.0578 3.6918 3.3276 2.8467 2.5292 2.2461 2.0057 1.745 1.5326 1.3179 1.1513 1.028 0.90093 0.77813 0.68866 0.60966 0.54931 0.48243 0.43547 0.38952 0.34448 0.31193 100.24 NaN 9.6035 9.5045 9.095 8.457 13.586 12.798 11.383 10.628 9.9318 9.1948 6.2465 6.1046 5.9906 5.9624 5.8924 5.8851 5.838 5.7496 5.743 5.6688 5.7657 5.7238 5.6154 5.4315 5.1992 4.9551 4.7044 4.273 3.8839 3.5011 3.1395 2.6889 2.3813 2.1328 1.8586 1.6161 1.433 1.2634 1.084 0.96689 0.86359 0.72942 0.66981 0.56846 0.51631 0.45716 0.41264 0.3716 0.35238 0.29838
x = T1{1,2:end};
y = T1{2:end,1};
z = fillmissing(T1{2:end,2:end}, 'nearest'); % Interpolate 'NaN' Elements
figure
mesh(x, y, z)
xlabel('X')
ylabel('Y')
zlabel('Z')
.

3 Kommentare
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Delonte Goodman
Delonte Goodman am 13 Jul. 2023
Thanks for the help. It looks like when I try your solution it is missing some data points.
Delonte Goodman
Delonte Goodman am 13 Jul. 2023
I am trying to get the plot look look something like the attached image.
Star Strider
Star Strider am 14 Jul. 2023
Bearbeitet: Star Strider am 14 Jul. 2023
Ran in:
Ran in:
I cannot duplicate that exactly, however I can get reasonably close.
Try this —
T1 = readtable('Epsilon_Prime.xlsx')
T1 = 96×52 table
Var1 Var2 Var3 Var4 Var5 Var6 Var7 Var8 Var9 Var10 Var11 Var12 Var13 Var14 Var15 Var16 Var17 Var18 Var19 Var20 Var21 Var22 Var23 Var24 Var25 Var26 Var27 Var28 Var29 Var30 Var31 Var32 Var33 Var34 Var35 Var36 Var37 Var38 Var39 Var40 Var41 Var42 Var43 Var44 Var45 Var46 Var47 Var48 Var49 Var50 Var51 Var52 ______ ____________ ______ ______ ______ ______ ______ ______ ______ ______ ______ ______ ______ ______ ______ ______ ______ ______ ______ ______ ______ ______ ______ ______ ______ ______ ______ ______ ______ ______ ______ ______ ______ ______ ______ ______ ______ ______ ______ ______ ______ _______ _______ _______ _______ _______ _______ _______ _______ _______ _______ _______ NaN {'160' } 158 156 154 152 150 148 146 144 142 140 138 136 134 132 130 128 126 124 122 120 118 116 114 112 110 108 106 104 102 100 98 96 94 92 90 88 86 84 82 80 78 76 74 72 70 68 66 64 62 60 20 {'nan' } NaN 5376.5 4420.5 3450.3 3299.9 2746.2 2471.8 2221.8 2112.4 2042.9 2012.1 1894.2 1929.5 1988.6 2044.6 2117.8 2187.4 2272.9 2364.9 2493.6 2672.2 2827.1 3045.7 3291.3 3618.7 3987.5 4468 5046.2 5737.1 6417.5 7385.6 NaN 9257.9 10261 11192 12226 13050 13758 14261 14243 13811 14021 13978 13440 13356 13123 13573 12693 12744 12161 22.44 {'17.52692'} 16.682 16.229 15.513 14.814 17.273 16.247 14.613 13.45 12.377 11.049 7.5733 6.9713 6.4226 5.9996 5.5385 5.2075 4.8715 4.6357 4.4398 4.2745 4.226 4.1981 4.1285 4.1565 4.1872 4.2628 4.2707 4.3972 4.4723 4.4926 4.5755 4.5357 4.4866 4.5057 4.3043 3.9741 3.4877 3.2215 2.8953 2.4634 2.1093 1.8325 1.6805 1.4388 1.2973 1.0391 0.9618 0.81599 0.72641 0.61435 25.179 {'nan' } 16.257 15.747 15.123 14.39 16.852 15.931 14.24 13.078 11.998 10.775 7.2923 6.7532 6.2304 5.8096 5.4412 5.1063 4.8377 4.5811 4.4363 4.2889 4.2585 4.2283 4.2084 4.2252 4.2628 4.3197 4.4501 4.5162 4.5737 4.6372 4.6715 4.443 4.3951 4.2685 3.8344 3.7848 3.3584 2.9628 2.5779 2.3207 1.9965 1.7474 1.5044 1.2989 1.1261 0.98147 0.85201 0.76012 0.66784 0.59448 28.251 {'nan' } 15.777 15.3 14.666 13.93 16.513 15.584 13.842 12.755 11.674 10.482 7.0428 6.5424 6.0642 5.6878 5.3285 5.0511 4.7949 4.5743 4.4362 4.321 4.3006 4.3014 4.2889 4.3286 4.3658 4.4403 4.5339 4.6716 4.6486 4.6768 4.7136 4.4015 4.3075 4.1149 3.757 3.6069 3.2197 2.7823 2.428 2.1371 1.8373 1.5828 1.4141 1.1906 1.016 0.90325 0.79465 0.71238 0.61572 0.55023 31.698 {'nan' } 15.262 14.82 14.207 13.436 16.177 15.233 13.548 12.438 11.376 10.177 6.849 6.3689 5.9209 5.5953 5.2592 4.9978 4.7786 4.5871 4.464 4.3537 4.3684 4.3808 4.4017 4.4507 4.4953 4.6389 4.6763 4.7346 4.7377 4.7729 4.6833 4.3715 4.2334 3.9305 3.5475 3.2924 2.9309 2.6171 2.2435 1.9859 1.6591 1.4614 1.2674 1.0903 0.95361 0.85184 0.74612 0.65334 0.58556 0.51652 35.566 {'nan' } 14.735 14.359 13.684 12.949 15.835 14.935 13.253 12.102 11.101 9.9636 6.6529 6.2191 5.8046 5.4979 5.2067 4.9945 4.7946 4.5709 4.4927 4.424 4.4442 4.4892 4.4928 4.5621 4.6094 4.7469 4.8516 4.7819 4.7527 4.7858 4.5932 4.2358 4.017 3.7528 3.494 3.0737 2.771 2.4239 2.0717 1.824 1.5836 1.3509 1.2035 1.0397 0.87759 0.78836 0.70779 0.61874 0.56684 0.49003 39.905 {'nan' } 14.172 13.767 13.277 12.486 15.443 14.565 12.909 11.856 10.81 9.7475 6.489 6.1072 5.7281 5.4798 5.1792 4.9818 4.8607 4.6284 4.5973 4.4787 4.493 4.5232 4.6659 4.7873 4.6949 4.7636 5.0295 4.774 4.8862 4.8209 4.4322 4.3568 3.7905 3.7466 3.0341 2.9156 2.4104 2.3544 1.9128 1.6361 1.4047 1.2034 1.1589 1.0076 0.87016 0.69415 0.65249 0.54458 0.53011 0.48351 44.774 {'nan' } 13.578 13.224 12.649 11.937 15.279 14.236 12.706 11.559 10.635 9.5921 6.3488 6.0191 5.6809 5.4067 5.1316 4.9822 4.8168 4.6503 4.6602 4.6386 4.6118 4.6488 4.7766 4.8578 4.724 5.1356 4.8483 4.9241 4.9154 4.3778 4.4654 4.0472 3.7151 3.3326 2.7393 2.6422 2.2453 2.1551 1.8712 1.466 1.202 1.0824 0.96656 0.88107 0.70471 0.76874 0.48341 0.43151 0.48964 0.30726 50.238 {'nan' } 13.181 12.657 12.035 11.307 14.747 13.922 12.411 11.294 10.538 9.3844 6.3283 5.9895 5.5952 5.4603 5.1404 5.1201 4.9845 4.774 4.8837 4.6371 4.6775 4.7317 4.8217 4.7839 4.7557 4.6974 5 5.4211 5.1567 4.8857 4.3749 3.4022 3.4062 3.0238 2.5556 2.2236 1.8802 2.2239 1.6354 1.3123 1.4197 1.2952 1.0333 0.6733 0.70021 0.66624 0.47653 0.4324 0.40965 0.37803 56.368 {'nan' } 12.453 12.199 11.631 10.871 14.611 13.731 12.119 11.122 10.197 9.1997 6.1608 5.8724 5.5744 5.3878 5.2531 5.1002 4.9818 4.8562 4.8556 4.8613 4.9171 5.0178 5.0621 5.1299 5.1227 5.1052 5.1229 4.7985 4.7768 4.476 4.025 3.5122 3.1307 3.0766 2.6014 2.1701 1.9231 1.8632 1.4606 1.2401 1.2209 1.036 0.82349 0.69554 0.84908 0.49442 0.36409 0.58977 0.59532 0.53822 63.246 {'nan' } 11.85 11.646 11.101 10.361 14.354 13.454 11.91 10.956 10.106 9.1185 6.1133 5.8441 5.5997 5.4702 5.29 5.2306 5.1451 5.008 4.9988 4.9739 5.1234 5.2222 5.1696 5.222 5.1467 5.177 5.1029 4.8543 4.4666 4.1844 3.9322 3.4455 3.1073 2.675 2.3884 2.1701 1.8817 1.6177 1.3643 1.2149 1.097 0.8983 0.75204 0.7112 0.59941 0.49777 0.50007 0.43015 0.35473 0.35647 70.963 {'nan' } 11.272 11.131 10.563 9.8645 14.017 13.251 11.773 10.907 9.9259 8.9618 6.0241 5.8335 5.597 5.4689 5.4775 5.2462 5.1924 5.1808 5.2764 5.2629 5.3448 5.2989 5.5159 5.542 5.1946 5.0397 4.9717 4.9653 4.663 4.1506 3.8881 3.1409 2.7407 2.7692 2.3558 1.8736 1.6288 1.4285 1.3477 1.0994 1.0693 0.9129 0.83627 0.66831 0.60707 0.45439 0.48332 0.4231 0.3083 0.2997 79.621 {'nan' } 10.7 10.54 10.099 9.3385 13.881 13.035 11.57 10.69 9.9195 9.0222 6.1052 5.8896 5.707 5.6338 5.5266 5.4632 5.4244 5.3443 5.3829 5.2937 5.3879 5.5386 5.4791 5.3686 5.3063 5.0993 4.8638 4.6757 4.2322 3.8743 3.4545 2.9151 2.6263 2.4567 2.0297 1.9898 1.6203 1.4012 1.2671 0.95795 1.0546 0.70973 0.85721 0.55748 0.62035 0.4175 0.48917 0.27234 0.24339 0.45411 89.337 {'nan' } 10.144 10.013 9.5853 8.8827 13.734 12.896 11.457 10.634 9.8922 9.0911 6.1461 5.9725 5.8421 5.771 5.6901 5.6415 5.6119 5.5235 5.5234 5.5077 5.6006 5.6284 5.5289 5.3919 5.2247 4.9932 4.7759 4.4167 4.0578 3.6918 3.3276 2.8467 2.5292 2.2461 2.0057 1.745 1.5326 1.3179 1.1513 1.028 0.90093 0.77813 0.68866 0.60966 0.54931 0.48243 0.43547 0.38952 0.34448 0.31193 100.24 {'nan' } 9.6035 9.5045 9.095 8.457 13.586 12.798 11.383 10.628 9.9318 9.1948 6.2465 6.1046 5.9906 5.9624 5.8924 5.8851 5.838 5.7496 5.743 5.6688 5.7657 5.7238 5.6154 5.4315 5.1992 4.9551 4.7044 4.273 3.8839 3.5011 3.1395 2.6889 2.3813 2.1328 1.8586 1.6161 1.433 1.2634 1.084 0.96689 0.86359 0.72942 0.66981 0.56846 0.51631 0.45716 0.41264 0.3716 0.35238 0.29838
T1.Var2 = str2double(T1.Var2)
T1 = 96×52 table
Var1 Var2 Var3 Var4 Var5 Var6 Var7 Var8 Var9 Var10 Var11 Var12 Var13 Var14 Var15 Var16 Var17 Var18 Var19 Var20 Var21 Var22 Var23 Var24 Var25 Var26 Var27 Var28 Var29 Var30 Var31 Var32 Var33 Var34 Var35 Var36 Var37 Var38 Var39 Var40 Var41 Var42 Var43 Var44 Var45 Var46 Var47 Var48 Var49 Var50 Var51 Var52 ______ ______ ______ ______ ______ ______ ______ ______ ______ ______ ______ ______ ______ ______ ______ ______ ______ ______ ______ ______ ______ ______ ______ ______ ______ ______ ______ ______ ______ ______ ______ ______ ______ ______ ______ ______ ______ ______ ______ ______ ______ _______ _______ _______ _______ _______ _______ _______ _______ _______ _______ _______ NaN 160 158 156 154 152 150 148 146 144 142 140 138 136 134 132 130 128 126 124 122 120 118 116 114 112 110 108 106 104 102 100 98 96 94 92 90 88 86 84 82 80 78 76 74 72 70 68 66 64 62 60 20 NaN NaN 5376.5 4420.5 3450.3 3299.9 2746.2 2471.8 2221.8 2112.4 2042.9 2012.1 1894.2 1929.5 1988.6 2044.6 2117.8 2187.4 2272.9 2364.9 2493.6 2672.2 2827.1 3045.7 3291.3 3618.7 3987.5 4468 5046.2 5737.1 6417.5 7385.6 NaN 9257.9 10261 11192 12226 13050 13758 14261 14243 13811 14021 13978 13440 13356 13123 13573 12693 12744 12161 22.44 17.527 16.682 16.229 15.513 14.814 17.273 16.247 14.613 13.45 12.377 11.049 7.5733 6.9713 6.4226 5.9996 5.5385 5.2075 4.8715 4.6357 4.4398 4.2745 4.226 4.1981 4.1285 4.1565 4.1872 4.2628 4.2707 4.3972 4.4723 4.4926 4.5755 4.5357 4.4866 4.5057 4.3043 3.9741 3.4877 3.2215 2.8953 2.4634 2.1093 1.8325 1.6805 1.4388 1.2973 1.0391 0.9618 0.81599 0.72641 0.61435 25.179 NaN 16.257 15.747 15.123 14.39 16.852 15.931 14.24 13.078 11.998 10.775 7.2923 6.7532 6.2304 5.8096 5.4412 5.1063 4.8377 4.5811 4.4363 4.2889 4.2585 4.2283 4.2084 4.2252 4.2628 4.3197 4.4501 4.5162 4.5737 4.6372 4.6715 4.443 4.3951 4.2685 3.8344 3.7848 3.3584 2.9628 2.5779 2.3207 1.9965 1.7474 1.5044 1.2989 1.1261 0.98147 0.85201 0.76012 0.66784 0.59448 28.251 NaN 15.777 15.3 14.666 13.93 16.513 15.584 13.842 12.755 11.674 10.482 7.0428 6.5424 6.0642 5.6878 5.3285 5.0511 4.7949 4.5743 4.4362 4.321 4.3006 4.3014 4.2889 4.3286 4.3658 4.4403 4.5339 4.6716 4.6486 4.6768 4.7136 4.4015 4.3075 4.1149 3.757 3.6069 3.2197 2.7823 2.428 2.1371 1.8373 1.5828 1.4141 1.1906 1.016 0.90325 0.79465 0.71238 0.61572 0.55023 31.698 NaN 15.262 14.82 14.207 13.436 16.177 15.233 13.548 12.438 11.376 10.177 6.849 6.3689 5.9209 5.5953 5.2592 4.9978 4.7786 4.5871 4.464 4.3537 4.3684 4.3808 4.4017 4.4507 4.4953 4.6389 4.6763 4.7346 4.7377 4.7729 4.6833 4.3715 4.2334 3.9305 3.5475 3.2924 2.9309 2.6171 2.2435 1.9859 1.6591 1.4614 1.2674 1.0903 0.95361 0.85184 0.74612 0.65334 0.58556 0.51652 35.566 NaN 14.735 14.359 13.684 12.949 15.835 14.935 13.253 12.102 11.101 9.9636 6.6529 6.2191 5.8046 5.4979 5.2067 4.9945 4.7946 4.5709 4.4927 4.424 4.4442 4.4892 4.4928 4.5621 4.6094 4.7469 4.8516 4.7819 4.7527 4.7858 4.5932 4.2358 4.017 3.7528 3.494 3.0737 2.771 2.4239 2.0717 1.824 1.5836 1.3509 1.2035 1.0397 0.87759 0.78836 0.70779 0.61874 0.56684 0.49003 39.905 NaN 14.172 13.767 13.277 12.486 15.443 14.565 12.909 11.856 10.81 9.7475 6.489 6.1072 5.7281 5.4798 5.1792 4.9818 4.8607 4.6284 4.5973 4.4787 4.493 4.5232 4.6659 4.7873 4.6949 4.7636 5.0295 4.774 4.8862 4.8209 4.4322 4.3568 3.7905 3.7466 3.0341 2.9156 2.4104 2.3544 1.9128 1.6361 1.4047 1.2034 1.1589 1.0076 0.87016 0.69415 0.65249 0.54458 0.53011 0.48351 44.774 NaN 13.578 13.224 12.649 11.937 15.279 14.236 12.706 11.559 10.635 9.5921 6.3488 6.0191 5.6809 5.4067 5.1316 4.9822 4.8168 4.6503 4.6602 4.6386 4.6118 4.6488 4.7766 4.8578 4.724 5.1356 4.8483 4.9241 4.9154 4.3778 4.4654 4.0472 3.7151 3.3326 2.7393 2.6422 2.2453 2.1551 1.8712 1.466 1.202 1.0824 0.96656 0.88107 0.70471 0.76874 0.48341 0.43151 0.48964 0.30726 50.238 NaN 13.181 12.657 12.035 11.307 14.747 13.922 12.411 11.294 10.538 9.3844 6.3283 5.9895 5.5952 5.4603 5.1404 5.1201 4.9845 4.774 4.8837 4.6371 4.6775 4.7317 4.8217 4.7839 4.7557 4.6974 5 5.4211 5.1567 4.8857 4.3749 3.4022 3.4062 3.0238 2.5556 2.2236 1.8802 2.2239 1.6354 1.3123 1.4197 1.2952 1.0333 0.6733 0.70021 0.66624 0.47653 0.4324 0.40965 0.37803 56.368 NaN 12.453 12.199 11.631 10.871 14.611 13.731 12.119 11.122 10.197 9.1997 6.1608 5.8724 5.5744 5.3878 5.2531 5.1002 4.9818 4.8562 4.8556 4.8613 4.9171 5.0178 5.0621 5.1299 5.1227 5.1052 5.1229 4.7985 4.7768 4.476 4.025 3.5122 3.1307 3.0766 2.6014 2.1701 1.9231 1.8632 1.4606 1.2401 1.2209 1.036 0.82349 0.69554 0.84908 0.49442 0.36409 0.58977 0.59532 0.53822 63.246 NaN 11.85 11.646 11.101 10.361 14.354 13.454 11.91 10.956 10.106 9.1185 6.1133 5.8441 5.5997 5.4702 5.29 5.2306 5.1451 5.008 4.9988 4.9739 5.1234 5.2222 5.1696 5.222 5.1467 5.177 5.1029 4.8543 4.4666 4.1844 3.9322 3.4455 3.1073 2.675 2.3884 2.1701 1.8817 1.6177 1.3643 1.2149 1.097 0.8983 0.75204 0.7112 0.59941 0.49777 0.50007 0.43015 0.35473 0.35647 70.963 NaN 11.272 11.131 10.563 9.8645 14.017 13.251 11.773 10.907 9.9259 8.9618 6.0241 5.8335 5.597 5.4689 5.4775 5.2462 5.1924 5.1808 5.2764 5.2629 5.3448 5.2989 5.5159 5.542 5.1946 5.0397 4.9717 4.9653 4.663 4.1506 3.8881 3.1409 2.7407 2.7692 2.3558 1.8736 1.6288 1.4285 1.3477 1.0994 1.0693 0.9129 0.83627 0.66831 0.60707 0.45439 0.48332 0.4231 0.3083 0.2997 79.621 NaN 10.7 10.54 10.099 9.3385 13.881 13.035 11.57 10.69 9.9195 9.0222 6.1052 5.8896 5.707 5.6338 5.5266 5.4632 5.4244 5.3443 5.3829 5.2937 5.3879 5.5386 5.4791 5.3686 5.3063 5.0993 4.8638 4.6757 4.2322 3.8743 3.4545 2.9151 2.6263 2.4567 2.0297 1.9898 1.6203 1.4012 1.2671 0.95795 1.0546 0.70973 0.85721 0.55748 0.62035 0.4175 0.48917 0.27234 0.24339 0.45411 89.337 NaN 10.144 10.013 9.5853 8.8827 13.734 12.896 11.457 10.634 9.8922 9.0911 6.1461 5.9725 5.8421 5.771 5.6901 5.6415 5.6119 5.5235 5.5234 5.5077 5.6006 5.6284 5.5289 5.3919 5.2247 4.9932 4.7759 4.4167 4.0578 3.6918 3.3276 2.8467 2.5292 2.2461 2.0057 1.745 1.5326 1.3179 1.1513 1.028 0.90093 0.77813 0.68866 0.60966 0.54931 0.48243 0.43547 0.38952 0.34448 0.31193 100.24 NaN 9.6035 9.5045 9.095 8.457 13.586 12.798 11.383 10.628 9.9318 9.1948 6.2465 6.1046 5.9906 5.9624 5.8924 5.8851 5.838 5.7496 5.743 5.6688 5.7657 5.7238 5.6154 5.4315 5.1992 4.9551 4.7044 4.273 3.8839 3.5011 3.1395 2.6889 2.3813 2.1328 1.8586 1.6161 1.433 1.2634 1.084 0.96689 0.86359 0.72942 0.66981 0.56846 0.51631 0.45716 0.41264 0.3716 0.35238 0.29838
x = T1{1,2:end};
y = T1{2:end,1};
[X,Y] = ndgrid(x,y);
z = fillmissing(T1{2:end,2:end}, 'nearest'); % Interpolate 'NaN' Elements
F = scatteredInterpolant(X(:),Y(:),z(:));
L74 = F(74,0)
L74 = 17.5910
L66 = F(66,0)
L66 = 31.8744
figure
surf(x, y, z)
colormap(turbo)
hold on
plot3([1 1]*74,[0 0],[0.1 L74], '-.b', 'LineWidth',2)
plot3([1 1]*66,[0 0],[0.1 L66], '-.b', 'LineWidth',2)
hold off
% colorbar
zlim([0.1 max(zlim)])
Ax = gca;
Ax.ZScale = 'log';
xlabel('Temperature (°C)')
ylabel('Frequency (Hz)')
zlabel('\epsilon''')
view(5,10)
text(74, 0, 0.05, '74 °C (N)', 'Color','r', 'Rotation',-30, 'Horiz','left')
text(66, 0, 0.05, '66 °C (NF)', 'Color','r', 'Rotation',-30, 'Horiz','left')
The scatteredInterpolant call is simply used to define the upper ends of the blue dash-dot lines. It is otherwise not necessary. SPecifically see the documentation for the view function to change the axes orientation.
Your data do not appear to be exactly those of the provided plot image, so this is the best I can do.
Experiment to get different results.
EDIT — (14 Jul 2023 at 01:47)
Changed the view arguments.
.

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Delonte Goodman
Delonte Goodman
am 12 Jul. 2023

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Star Strider
am 14 Jul. 2023

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