Best fit line for scatter data along y=x line

Question

Rohit 2024-9-7

0
链接

此问题的直接链接

https://ww2.mathworks.cn/matlabcentral/answers/2151039-best-fit-line-for-scatter-data-along-y-x-line

评论： Star Strider 2024-9-9

I have a scatter data point (file name PntP) and corresponding temperature values (file name uu) for different time levels. In the data file 'PntP' the PntP(:,1) is 'x' coordinate and PntP(:,2) is 'y' coordinate. In data file 'uu' each column represents the temperature values corresponding to given data points at time levels. Please give me idea how to find the temperature plot along the line y=mx pasing through coordinates (x1,y1) and (x2,y2) for a fix time stations.

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Answer 1

Star Strider 2024-9-7

0
链接

此回答的直接链接

https://ww2.mathworks.cn/matlabcentral/answers/2151039-best-fit-line-for-scatter-data-along-y-x-line#answer_1512654

在 MATLAB Online 中打开

What sort of regression would you want for the volume in the second figure?

LD1= load('uu.mat');

uu = LD1.uu;

uu_size = size(uu)

uu_size = 1x2

4381 51

LD2 = load('PntP.mat');

x = LD2.PntP(:,1);

y = LD2.PntP(:,2)'

y = 1x4381

0.0155 0.0160 0.0140 0.0145 0.0150 0.0155 0.0160 0.0165 0.0170 0.0175 0.0180 0.0130 0.0135 0.0140 0.0145 0.0150 0.0155 0.0160 0.0165 0.0170 0.0175 0.0180 0.0185 0.0190 0.0125 0.0130 0.0135 0.0140 0.0145 0.0150

<mw-icon class=""></mw-icon>

figure

scatter(x, y, '.')

grid

figure

hold on

for k = 1:size(uu,2)

scatter3(x, y, uu(:,k), '.')

end

hold off

grid on

xlabel('X')

ylabel('Y')

zlabel('UU')

view(-27,30)

The linear regressions themselves would likely not be difficult, however how would they be defined iin therms of ‘x’, ‘y’, and the columns of ‘uu’?

.

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Star Strider 2024-9-8

在 MATLAB Online 中打开

I still do not understand.

See if this works —

LD1= load('uu.mat');

uu = LD1.uu;

uu_size = size(uu);

LD2 = load('PntP.mat');

x = LD2.PntP(:,1);

y = LD2.PntP(:,2);

DM = [x ones(size(x))]; % Design Matrix

B = DM \ y;

RL = DM * B; % Regreession Line

figure

scatter(x, y, '.', 'DisplayName','Data')

hold on

plot(x, RL, '-r', 'LineWidth',2, 'DisplayName','Linear Regression Line')

hold off

grid

legend('Location','best')

% text(max(x), max(RL), sprintf('$y = %.5f\\cdot x %+.5f$',B), 'Horiz','left', 'Interpreter','LaTeX')

text(0.03, 0.035, sprintf('$y = %.5f\\cdot x %+.5f$',B), 'Horiz','left', 'Interpreter','LaTeX')

xv = repmat(x, size(uu,2), 1);

RLv = repmat(RL, size(uu,2), 1);

UUfcn = scatteredInterpolant(xv, RLv, uu(:));

Warning: Duplicate data points have been detected and removed - corresponding values have been averaged.

uuv = UUfcn(x,RL);

figure

hold on

for k = 1:size(uu,2)

hs3(k) = scatter3(x, y, uu(:,k), '.', 'DisplayName','Data');

end

hp3 = plot3(x, RL, uuv, '-r', 'LineWidth',5, 'DisplayName','Regression Line (Spatial)');

hold off

grid on

xlabel('X')

ylabel('Y')

zlabel('UU')

view(-27,30)

legend(hp3, 'Location','best')

figure

% hold on

% for k = 1:size(uu,2)

% scatter3(x, y, uu(:,k), '.')

% end

plot3(x, RL, uuv, '-r', 'LineWidth',5, 'DisplayName','Regression Line (Spatial)')

% hold off

grid on

xlabel('X')

ylabel('Y')

zlabel('UU')

view(-27,30)

xlabel('X')

ylabel('Y')

zlabel('UU')

title('Isolated 3D Regression Line')

Result = table(x, y, uuv)

Result = 4381x3 table

x y uuv ______ ______ ______ 0.0035 0.0155 36.769 0.0035 0.016 36.769 0.004 0.014 35.236 0.004 0.0145 35.236 0.004 0.015 35.236 0.004 0.0155 35.236 0.004 0.016 35.236 0.004 0.0165 35.236 0.004 0.017 35.236 0.004 0.0175 35.236 0.004 0.018 35.236 0.0045 0.013 33.829 0.0045 0.0135 33.829 0.0045 0.014 33.829 0.0045 0.0145 33.829 0.0045 0.015 33.829

The values for the regression line are in the ‘uuv’ variable.

.

Star Strider 2024-9-9

编辑：Star Strider 2024-9-9

在 MATLAB Online 中打开

This took lonoger than I ever could have imagined.

To briefly describe how iit works, it first calculates the boundary and thee centroid, then uses them to find the midliine indices of the selected valuees of ‘y’ that define the boundary, and from them index vector, ‘idxv’. It then uses ‘idxv’ to select the ‘x’ values of tthe boundary and the corresponding ‘y’ values of the boundary to define the linear regression of all lines joining the opposite sides of the boundary and that pass through the centroid. It does this by defining a design matrix ‘DM’ and then performing the linear regression. (See the documentation on mldivide, \ to understand how that works.) It then stores the ‘x’ values used in the regressiono (as ‘xr’) and correspoiinding ‘y’ values (computed from the regression) as ‘yr’. (It also stores the regression coefficients ‘B’ for later reference, if needed.) When completed, it has computed 399 regressions, with their corresponding ‘x’ and ‘y’ values of the boundary.

It then calculates the interpolation funciton ‘UUfcn’ using the available ‘x’ and ‘y’ data and ‘uu(:50)’. The second loop creeates vectors ‘xq’ and ‘yq’ (arbitrarily 100 elements each) and stores them as ‘xp’ and ‘yp’ for later reference, and uses them to calculate the hypotenuse of the triangle they create to use as the independent variable vector for the trapz calculation of the area under the ‘z’ curve.. The ‘z’ curve is created by using ‘xq’ and ‘yq’ as arguments to ‘UUfcn’, to interpolate the ‘uu(:50)’ data along the line created by them. It then use the ‘profile’ cell array to store the necessary data, does some simple analyses (minimum, maximum, mean, RMS value, and area under the curve). You can do whatever additional analyses you want, using the information in the ‘profile’ array.

IIt then plots all the 399 regreession lines it calculates (the magenta lines), as well as selected regressionl lines from the calculatiion loop in red to demonstrate what it actually does. It plots the calculated centroid as a green pentagram.

And with that, I am finished with this!

LD1= load('uu.mat');

uu = LD1.uu;

uu_size = size(uu);

LD2 = load('PntP.mat');

x = LD2.PntP(:,1);

y = LD2.PntP(:,2);

kb = boundary(x, y, 0.965);

BndryLen = numel(kb);

[xl(1),xl(2)] = bounds(x(kb))

xl = 1x2

0.0034 0.0393

xl = 1x2

0.0034 0.0393

[yl(1),yl(2)] = bounds(y(kb))

yl = 1x2

0.0011 0.0371

yl = 1x2

0.0011 0.0371

ctrd = @(x,y) [trapz(x, x.*y) / trapz(x, y); trapz(y, x.*y) / trapz(y, x)];

cxcy = ctrd(x(kb), y(kb)); % Centroid Coordiates

cx = cxcy(1);

cy = cxcy(2);

Lvml = ismembertol(y(kb), cx, 1E-4);

mlidx = find(Lvml); % Midline 'x' Indices

% CheckCoords = [x(kb(mlidx([1 2]))) y(kb(mlidx([1 2])))]

% CheckCoords2 = [x(kb(mlidx([1 2])+25)) y(kb(mlidx([1 2])+25))]

idxv = mlidx(1) : mlidx(2);

for k1 = 1:numel(idxv)

% k1

idxrng = mlidx([1 2])+k1;

DM = [x(kb(idxrng)) ones(size(x(kb(idxrng))))]; % Design Matrix

% Q3 = [x(kb(idxrng)) y(kb(idxrng))];

B(:,k1) = DM \ y(kb(idxrng));

xr(:,k1) = x(kb(idxrng));

yr(:,k1) = DM * B(:,k1); % Regreession Line

end

B

B = 2x399

0.0079 0.0157 0.0236 0.0314 0.0393 0.0472 0.0550 0.0629 0.0708 0.0787 0.0866 0.0945 0.1025 0.1104 0.1184 0.1263 0.1343 0.1423 0.1503 0.1584 0.1664 0.1745 0.1826 0.1908 0.1989 0.2071 0.2153 0.2235 0.2318 0.2401 0.0198 0.0197 0.0195 0.0194 0.0192 0.0191 0.0189 0.0187 0.0186 0.0184 0.0183 0.0181 0.0180 0.0178 0.0176 0.0175 0.0173 0.0172 0.0170 0.0168 0.0167 0.0165 0.0163 0.0162 0.0160 0.0159 0.0157 0.0155 0.0154 0.0152

<mw-icon class=""></mw-icon>

Bref = [1:size(B,2); B];

disp(Bref)

Columns 1 through 18 1.0000 2.0000 3.0000 4.0000 5.0000 6.0000 7.0000 8.0000 9.0000 10.0000 11.0000 12.0000 13.0000 14.0000 15.0000 16.0000 17.0000 18.0000 0.0079 0.0157 0.0236 0.0314 0.0393 0.0472 0.0550 0.0629 0.0708 0.0787 0.0866 0.0945 0.1025 0.1104 0.1184 0.1263 0.1343 0.1423 0.0198 0.0197 0.0195 0.0194 0.0192 0.0191 0.0189 0.0187 0.0186 0.0184 0.0183 0.0181 0.0180 0.0178 0.0176 0.0175 0.0173 0.0172 Columns 19 through 36 19.0000 20.0000 21.0000 22.0000 23.0000 24.0000 25.0000 26.0000 27.0000 28.0000 29.0000 30.0000 31.0000 32.0000 33.0000 34.0000 35.0000 36.0000 0.1503 0.1584 0.1664 0.1745 0.1826 0.1908 0.1989 0.2071 0.2153 0.2235 0.2318 0.2401 0.2484 0.2568 0.2651 0.2736 0.2820 0.2905 0.0170 0.0168 0.0167 0.0165 0.0163 0.0162 0.0160 0.0159 0.0157 0.0155 0.0154 0.0152 0.0150 0.0149 0.0147 0.0145 0.0144 0.0142 Columns 37 through 54 37.0000 38.0000 39.0000 40.0000 41.0000 42.0000 43.0000 44.0000 45.0000 46.0000 47.0000 48.0000 49.0000 50.0000 51.0000 52.0000 53.0000 54.0000 0.2991 0.3076 0.3163 0.3249 0.3336 0.3424 0.3512 0.3600 0.3689 0.3779 0.3869 0.3959 0.4050 0.4142 0.4234 0.4327 0.4421 0.4515 0.0140 0.0138 0.0137 0.0135 0.0133 0.0132 0.0130 0.0128 0.0126 0.0124 0.0123 0.0121 0.0119 0.0117 0.0115 0.0113 0.0112 0.0110 Columns 55 through 72 55.0000 56.0000 57.0000 58.0000 59.0000 60.0000 61.0000 62.0000 63.0000 64.0000 65.0000 66.0000 67.0000 68.0000 69.0000 70.0000 71.0000 72.0000 0.4610 0.4706 0.4802 0.4899 0.4997 0.5095 0.5195 0.5295 0.5396 0.5498 0.5600 0.5704 0.5808 0.5914 0.6020 0.6128 0.6237 0.6346 0.0108 0.0106 0.0104 0.0102 0.0100 0.0098 0.0096 0.0094 0.0092 0.0090 0.0088 0.0086 0.0084 0.0082 0.0080 0.0077 0.0075 0.0073 Columns 73 through 90 73.0000 74.0000 75.0000 76.0000 77.0000 78.0000 79.0000 80.0000 81.0000 82.0000 83.0000 84.0000 85.0000 86.0000 87.0000 88.0000 89.0000 90.0000 0.6457 0.6569 0.6682 0.6796 0.6911 0.7028 0.7146 0.7265 0.7386 0.7508 0.7632 0.7757 0.7883 0.8012 0.8141 0.8273 0.8406 0.8541 0.0071 0.0069 0.0066 0.0064 0.0062 0.0059 0.0057 0.0055 0.0052 0.0050 0.0047 0.0045 0.0042 0.0040 0.0037 0.0035 0.0032 0.0029 Columns 91 through 108 91.0000 92.0000 93.0000 94.0000 95.0000 96.0000 97.0000 98.0000 99.0000 100.0000 101.0000 102.0000 103.0000 104.0000 105.0000 106.0000 107.0000 108.0000 0.8678 0.8816 0.8957 0.9099 0.9244 0.9391 0.9540 0.9691 0.9844 1.0000 1.0158 1.0319 1.0483 1.0649 1.0896 1.1069 1.1245 1.1425 0.0026 0.0024 0.0021 0.0018 0.0015 0.0012 0.0009 0.0006 0.0003 0.0000 -0.0003 -0.0006 -0.0010 -0.0013 -0.0017 -0.0021 -0.0024 -0.0028 Columns 109 through 126 109.0000 110.0000 111.0000 112.0000 113.0000 114.0000 115.0000 116.0000 117.0000 118.0000 119.0000 120.0000 121.0000 122.0000 123.0000 124.0000 125.0000 126.0000 1.1607 1.1793 1.1983 1.2176 1.2373 1.2573 1.2778 1.2987 1.3200 1.3417 1.3640 1.3867 1.4099 1.4336 1.4579 1.4827 1.5081 1.5341 -0.0031 -0.0035 -0.0039 -0.0043 -0.0047 -0.0051 -0.0055 -0.0059 -0.0063 -0.0067 -0.0072 -0.0076 -0.0081 -0.0086 -0.0090 -0.0095 -0.0100 -0.0106 Columns 127 through 144 127.0000 128.0000 129.0000 130.0000 131.0000 132.0000 133.0000 134.0000 135.0000 136.0000 137.0000 138.0000 139.0000 140.0000 141.0000 142.0000 143.0000 144.0000 1.5608 1.5881 1.6162 1.6449 1.6744 1.7047 1.7358 1.7677 1.8006 1.8344 1.8692 1.9050 1.9419 1.9800 2.0193 2.0598 2.1017 2.1450 -0.0111 -0.0116 -0.0122 -0.0128 -0.0134 -0.0140 -0.0146 -0.0152 -0.0159 -0.0166 -0.0172 -0.0180 -0.0187 -0.0195 -0.0202 -0.0210 -0.0219 -0.0227 Columns 145 through 162 145.0000 146.0000 147.0000 148.0000 149.0000 150.0000 151.0000 152.0000 153.0000 154.0000 155.0000 156.0000 157.0000 158.0000 159.0000 160.0000 161.0000 162.0000 2.1897 2.2361 2.2841 2.3338 2.3854 2.4390 2.4947 2.5526 2.6128 2.6756 2.7411 2.8095 2.8810 2.9558 3.0341 3.1163 3.2026 3.2933 -0.0236 -0.0246 -0.0255 -0.0265 -0.0275 -0.0286 -0.0297 -0.0309 -0.0321 -0.0333 -0.0346 -0.0360 -0.0374 -0.0389 -0.0405 -0.0421 -0.0438 -0.0456 Columns 163 through 180 163.0000 164.0000 165.0000 166.0000 167.0000 168.0000 169.0000 170.0000 171.0000 172.0000 173.0000 174.0000 175.0000 176.0000 177.0000 178.0000 179.0000 180.0000 3.3889 3.4897 3.5962 3.7089 3.8283 3.9552 4.0902 4.2342 4.3881 4.5530 4.7301 4.9210 5.1271 5.3506 5.5938 5.8593 6.1504 6.4712 -0.0475 -0.0495 -0.0517 -0.0539 -0.0563 -0.0588 -0.0615 -0.0644 -0.0674 -0.0707 -0.0743 -0.0781 -0.0822 -0.0866 -0.0915 -0.0968 -0.1026 -0.1090 Columns 181 through 198 181.0000 182.0000 183.0000 184.0000 185.0000 186.0000 187.0000 188.0000 189.0000 190.0000 191.0000 192.0000 193.0000 194.0000 195.0000 196.0000 197.0000 198.0000 6.8263 7.2217 7.6647 8.1645 8.7329 9.3851 10.1412 11.0283 12.0839 13.3612 14.9383 16.9353 19.5457 23.1043 28.2428 36.3152 50.8404 84.7139 -0.1160 -0.1239 -0.1328 -0.1427 -0.1540 -0.1670 -0.1821 -0.1998 -0.2208 -0.2463 -0.2777 -0.3176 -0.3696 -0.4405 -0.5430 -0.7039 -0.9934 -1.6687 Columns 199 through 216 199.0000 200.0000 201.0000 202.0000 203.0000 204.0000 205.0000 206.0000 207.0000 208.0000 209.0000 210.0000 211.0000 212.0000 213.0000 214.0000 215.0000 216.0000 253.7531 -254.9471 -84.8450 -50.8865 -36.3379 -28.2559 -23.1125 -19.5511 -16.9389 -14.9408 -13.3628 -12.0850 -11.0290 -10.1415 -9.3851 -8.7327 -8.1642 -7.6642 -5.0384 5.1023 1.7114 1.0344 0.7444 0.5833 0.4808 0.4098 0.3577 0.3179 0.2864 0.2609 0.2399 0.2222 0.2071 0.1941 0.1828 0.1728 Columns 217 through 234 217.0000 218.0000 219.0000 220.0000 221.0000 222.0000 223.0000 224.0000 225.0000 226.0000 227.0000 228.0000 229.0000 230.0000 231.0000 232.0000 233.0000 234.0000 -7.2211 -6.8256 -6.4705 -6.1497 -5.8585 -5.5930 -5.3498 -5.1263 -4.9201 -4.7292 -4.5521 -4.3872 -4.2333 -4.0893 -3.9543 -3.8275 -3.7080 -3.5954 0.1640 0.1561 0.1490 0.1426 0.1368 0.1315 0.1267 0.1222 0.1181 0.1143 0.1108 0.1075 0.1044 0.1015 0.0988 0.0963 0.0939 0.0917 Columns 235 through 252 235.0000 236.0000 237.0000 238.0000 239.0000 240.0000 241.0000 242.0000 243.0000 244.0000 245.0000 246.0000 247.0000 248.0000 249.0000 250.0000 251.0000 252.0000 -3.4889 -3.3881 -3.2925 -3.2018 -3.0777 -2.9974 -2.9208 -2.8476 -2.7776 -2.7106 -2.6464 -2.5848 -2.5257 -2.4689 -2.4142 -2.3616 -2.3109 -2.2620 0.0896 0.0876 0.0856 0.0838 0.0816 0.0799 0.0784 0.0770 0.0756 0.0742 0.0729 0.0717 0.0705 0.0694 0.0683 0.0672 0.0662 0.0652 Columns 253 through 270 253.0000 254.0000 255.0000 256.0000 257.0000 258.0000 259.0000 260.0000 261.0000 262.0000 263.0000 264.0000 265.0000 266.0000 267.0000 268.0000 269.0000 270.0000 -2.2148 -2.1692 -2.1251 -2.0825 -2.0413 -2.0013 -1.9626 -1.9251 -1.8887 -1.8533 -1.8190 -1.7856 -1.7532 -1.7216 -1.6909 -1.6610 -1.6319 -1.6034 0.0643 0.0634 0.0625 0.0616 0.0608 0.0600 0.0593 0.0585 0.0578 0.0571 0.0564 0.0557 0.0551 0.0544 0.0538 0.0532 0.0526 0.0521 Columns 271 through 288 271.0000 272.0000 273.0000 274.0000 275.0000 276.0000 277.0000 278.0000 279.0000 280.0000 281.0000 282.0000 283.0000 284.0000 285.0000 286.0000 287.0000 288.0000 -1.5757 -1.5487 -1.5224 -1.4966 -1.4715 -1.4469 -1.4229 -1.3994 -1.3764 -1.3539 -1.3319 -1.3103 -1.2892 -1.2685 -1.2482 -1.2283 -1.2088 -1.1896 0.0515 0.0510 0.0504 0.0499 0.0494 0.0489 0.0485 0.0480 0.0475 0.0471 0.0466 0.0462 0.0458 0.0454 0.0450 0.0446 0.0442 0.0438 Columns 289 through 306 289.0000 290.0000 291.0000 292.0000 293.0000 294.0000 295.0000 296.0000 297.0000 298.0000 299.0000 300.0000 301.0000 302.0000 303.0000 304.0000 305.0000 306.0000 -1.1708 -1.1524 -1.1343 -1.1165 -1.0990 -1.0818 -1.0649 -1.0483 -1.0319 -1.0158 -1.0000 -0.9844 -0.9691 -0.9540 -0.9391 -0.9244 -0.9099 -0.8957 0.0434 0.0430 0.0427 0.0423 0.0420 0.0416 0.0413 0.0410 0.0406 0.0403 0.0400 0.0397 0.0394 0.0391 0.0388 0.0385 0.0382 0.0379 Columns 307 through 324 307.0000 308.0000 309.0000 310.0000 311.0000 312.0000 313.0000 314.0000 315.0000 316.0000 317.0000 318.0000 319.0000 320.0000 321.0000 322.0000 323.0000 324.0000 -0.8816 -0.8678 -0.8541 -0.8406 -0.8273 -0.8141 -0.8012 -0.7883 -0.7757 -0.7632 -0.7508 -0.7386 -0.7265 -0.7146 -0.7028 -0.6911 -0.6796 -0.6682 0.0376 0.0374 0.0371 0.0368 0.0365 0.0363 0.0360 0.0358 0.0355 0.0353 0.0350 0.0348 0.0345 0.0343 0.0341 0.0338 0.0336 0.0334 Columns 325 through 342 325.0000 326.0000 327.0000 328.0000 329.0000 330.0000 331.0000 332.0000 333.0000 334.0000 335.0000 336.0000 337.0000 338.0000 339.0000 340.0000 341.0000 342.0000 -0.6569 -0.6457 -0.6346 -0.6237 -0.6128 -0.6020 -0.5914 -0.5808 -0.5704 -0.5600 -0.5498 -0.5396 -0.5295 -0.5195 -0.5095 -0.4997 -0.4899 -0.4802 0.0331 0.0329 0.0327 0.0325 0.0323 0.0320 0.0318 0.0316 0.0314 0.0312 0.0310 0.0308 0.0306 0.0304 0.0302 0.0300 0.0298 0.0296 Columns 343 through 360 343.0000 344.0000 345.0000 346.0000 347.0000 348.0000 349.0000 350.0000 351.0000 352.0000 353.0000 354.0000 355.0000 356.0000 357.0000 358.0000 359.0000 360.0000 -0.4706 -0.4610 -0.4515 -0.4421 -0.4327 -0.4234 -0.4142 -0.4050 -0.3959 -0.3869 -0.3779 -0.3689 -0.3600 -0.3512 -0.3424 -0.3336 -0.3249 -0.3163 0.0294 0.0292 0.0290 0.0288 0.0287 0.0285 0.0283 0.0281 0.0279 0.0277 0.0276 0.0274 0.0272 0.0270 0.0268 0.0267 0.0265 0.0263 Columns 361 through 378 361.0000 362.0000 363.0000 364.0000 365.0000 366.0000 367.0000 368.0000 369.0000 370.0000 371.0000 372.0000 373.0000 374.0000 375.0000 376.0000 377.0000 378.0000 -0.3076 -0.2991 -0.2905 -0.2820 -0.2736 -0.2651 -0.2568 -0.2484 -0.2401 -0.2318 -0.2198 -0.2116 -0.2034 -0.1952 -0.1871 -0.1790 -0.1709 -0.1628 0.0262 0.0260 0.0258 0.0256 0.0255 0.0253 0.0251 0.0250 0.0248 0.0246 0.0243 0.0242 0.0240 0.0238 0.0237 0.0235 0.0234 0.0232 Columns 379 through 396 379.0000 380.0000 381.0000 382.0000 383.0000 384.0000 385.0000 386.0000 387.0000 388.0000 389.0000 390.0000 391.0000 392.0000 393.0000 394.0000 395.0000 396.0000 -0.1547 -0.1467 -0.1387 -0.1307 -0.1227 -0.1148 -0.1068 -0.0989 -0.0910 -0.0830 -0.0751 -0.0673 -0.0594 -0.0515 -0.0436 -0.0357 -0.0279 -0.0200 0.0230 0.0229 0.0227 0.0226 0.0224 0.0222 0.0221 0.0219 0.0218 0.0216 0.0214 0.0213 0.0211 0.0210 0.0208 0.0207 0.0205 0.0203 Columns 397 through 399 397.0000 398.0000 399.0000 -0.0122 -0.0043 0.0035 0.0202 0.0200 0.0199

xr

xr = 2x399

0.0384 0.0385 0.0386 0.0386 0.0387 0.0388 0.0388 0.0389 0.0389 0.0390 0.0390 0.0391 0.0391 0.0391 0.0392 0.0392 0.0392 0.0392 0.0393 0.0393 0.0393 0.0393 0.0392 0.0392 0.0392 0.0392 0.0392 0.0391 0.0391 0.0390 0.0049 0.0048 0.0048 0.0047 0.0046 0.0045 0.0045 0.0044 0.0043 0.0042 0.0042 0.0041 0.0040 0.0040 0.0039 0.0039 0.0038 0.0038 0.0037 0.0037 0.0036 0.0036 0.0036 0.0035 0.0035 0.0035 0.0035 0.0034 0.0034 0.0034

<mw-icon class=""></mw-icon>

yr

yr = 2x399

0.0201 0.0203 0.0204 0.0206 0.0207 0.0209 0.0210 0.0212 0.0213 0.0215 0.0216 0.0218 0.0220 0.0221 0.0223 0.0224 0.0226 0.0227 0.0229 0.0231 0.0232 0.0234 0.0235 0.0237 0.0238 0.0240 0.0241 0.0243 0.0244 0.0246 0.0199 0.0198 0.0196 0.0195 0.0194 0.0193 0.0191 0.0190 0.0189 0.0188 0.0186 0.0185 0.0184 0.0182 0.0181 0.0180 0.0178 0.0177 0.0176 0.0174 0.0173 0.0171 0.0170 0.0169 0.0167 0.0166 0.0164 0.0163 0.0162 0.0160

<mw-icon class=""></mw-icon>

xv = x;

yv = y;

UUfcn = scatteredInterpolant(xv, yv, uu(:,50));

for k1 = 1:size(xr,2)

col = k1;

xq = linspace(xr(1,k1), xr(2,k1)).';

yq = linspace(yr(1,k1), yr(2,k1)).';

xp{k1} = xq;

yp{k1} = yq;

bl = hypot(xq, yq);

z = UUfcn(xq, yq);

blz = rmmissing([bl z]);

AUC = trapz(blz(:,1), blz(:,2));

profile{k1} = {xq, yq, z};

zstats(:,k1) = [min(z); max(z); mean(z,'omitmissing'); sqrt(mean(z.^2,'omitmissing')); AUC];

end

profile{1}{1} % Get The Information From The 'profile' Array

ans = 100x1

0.0384 0.0381 0.0377 0.0374 0.0371 0.0367 0.0364 0.0360 0.0357 0.0354

<mw-icon class=""></mw-icon>

profile{1}{2}

ans = 100x1

0.0201 0.0201 0.0201 0.0201 0.0201 0.0201 0.0201 0.0201 0.0201 0.0201

<mw-icon class=""></mw-icon>

profile{1}{3}

ans = 100x1

37.0000 34.8113 32.5516 30.4859 28.7377 27.6519 26.8135 26.1216 25.3872 24.3834

<mw-icon class=""></mw-icon>

Result = array2table(zstats, 'VariableNames',compose('Col %3d',1:size(xr,2)), 'RowNames',{'Min','Max','Mean','RMS','AUC'})

Result = 5x399 table

Col 1 Col 2 Col 3 Col 4 Col 5 Col 6 Col 7 Col 8 Col 9 Col 10 Col 11 Col 12 Col 13 Col 14 Col 15 Col 16 Col 17 Col 18 Col 19 Col 20 Col 21 Col 22 Col 23 Col 24 Col 25 Col 26 Col 27 Col 28 Col 29 Col 30 Col 31 Col 32 Col 33 Col 34 Col 35 Col 36 Col 37 Col 38 Col 39 Col 40 Col 41 Col 42 Col 43 Col 44 Col 45 Col 46 Col 47 Col 48 Col 49 Col 50 Col 51 Col 52 Col 53 Col 54 Col 55 Col 56 Col 57 Col 58 Col 59 Col 60 Col 61 Col 62 Col 63 Col 64 Col 65 Col 66 Col 67 Col 68 Col 69 Col 70 Col 71 Col 72 Col 73 Col 74 Col 75 Col 76 Col 77 Col 78 Col 79 Col 80 Col 81 Col 82 Col 83 Col 84 Col 85 Col 86 Col 87 Col 88 Col 89 Col 90 Col 91 Col 92 Col 93 Col 94 Col 95 Col 96 Col 97 Col 98 Col 99 Col 100 Col 101 Col 102 Col 103 Col 104 Col 105 Col 106 Col 107 Col 108 Col 109 Col 110 Col 111 Col 112 Col 113 Col 114 Col 115 Col 116 Col 117 Col 118 Col 119 Col 120 Col 121 Col 122 Col 123 Col 124 Col 125 Col 126 Col 127 Col 128 Col 129 Col 130 Col 131 Col 132 Col 133 Col 134 Col 135 Col 136 Col 137 Col 138 Col 139 Col 140 Col 141 Col 142 Col 143 Col 144 Col 145 Col 146 Col 147 Col 148 Col 149 Col 150 Col 151 Col 152 Col 153 Col 154 Col 155 Col 156 Col 157 Col 158 Col 159 Col 160 Col 161 Col 162 Col 163 Col 164 Col 165 Col 166 Col 167 Col 168 Col 169 Col 170 Col 171 Col 172 Col 173 Col 174 Col 175 Col 176 Col 177 Col 178 Col 179 Col 180 Col 181 Col 182 Col 183 Col 184 Col 185 Col 186 Col 187 Col 188 Col 189 Col 190 Col 191 Col 192 Col 193 Col 194 Col 195 Col 196 Col 197 Col 198 Col 199 Col 200 Col 201 Col 202 Col 203 Col 204 Col 205 Col 206 Col 207 Col 208 Col 209 Col 210 Col 211 Col 212 Col 213 Col 214 Col 215 Col 216 Col 217 Col 218 Col 219 Col 220 Col 221 Col 222 Col 223 Col 224 Col 225 Col 226 Col 227 Col 228 Col 229 Col 230 Col 231 Col 232 Col 233 Col 234 Col 235 Col 236 Col 237 Col 238 Col 239 Col 240 Col 241 Col 242 Col 243 Col 244 Col 245 Col 246 Col 247 Col 248 Col 249 Col 250 Col 251 Col 252 Col 253 Col 254 Col 255 Col 256 Col 257 Col 258 Col 259 Col 260 Col 261 Col 262 Col 263 Col 264 Col 265 Col 266 Col 267 Col 268 Col 269 Col 270 Col 271 Col 272 Col 273 Col 274 Col 275 Col 276 Col 277 Col 278 Col 279 Col 280 Col 281 Col 282 Col 283 Col 284 Col 285 Col 286 Col 287 Col 288 Col 289 Col 290 Col 291 Col 292 Col 293 Col 294 Col 295 Col 296 Col 297 Col 298 Col 299 Col 300 Col 301 Col 302 Col 303 Col 304 Col 305 Col 306 Col 307 Col 308 Col 309 Col 310 Col 311 Col 312 Col 313 Col 314 Col 315 Col 316 Col 317 Col 318 Col 319 Col 320 Col 321 Col 322 Col 323 Col 324 Col 325 Col 326 Col 327 Col 328 Col 329 Col 330 Col 331 Col 332 Col 333 Col 334 Col 335 Col 336 Col 337 Col 338 Col 339 Col 340 Col 341 Col 342 Col 343 Col 344 Col 345 Col 346 Col 347 Col 348 Col 349 Col 350 Col 351 Col 352 Col 353 Col 354 Col 355 Col 356 Col 357 Col 358 Col 359 Col 360 Col 361 Col 362 Col 363 Col 364 Col 365 Col 366 Col 367 Col 368 Col 369 Col 370 Col 371 Col 372 Col 373 Col 374 Col 375 Col 376 Col 377 Col 378 Col 379 Col 380 Col 381 Col 382 Col 383 Col 384 Col 385 Col 386 Col 387 Col 388 Col 389 Col 390 Col 391 Col 392 Col 393 Col 394 Col 395 Col 396 Col 397 Col 398 Col 399 _______ _______ _______ _______ _______ _______ _______ _______ _______ _______ _______ _______ _______ _______ _______ _______ _______ _______ _______ _______ _______ _______ _______ _______ _______ _______ _______ _______ _______ _______ _______ _______ _______ _______ _______ _______ _______ _______ _______ _______ _______ _______ _______ _______ _______ _______ _______ _______ _______ _______ _______ _______ _______ _______ _______ _______ _______ _______ _______ _______ _______ _______ _______ _______ _______ _______ _______ _______ _______ _______ _______ _______ _______ _______ _______ _______ _______ _______ _______ _______ _______ _______ _______ _______ _______ _______ _______ _______ _______ _______ _______ _______ _______ _______ _______ _______ _______ _______ _______ _______ _______ _______ _______ _______ _______ _______ _______ _______ _______ _______ _______ _______ _______ _______ _______ _______ _______ _______ _______ _______ _______ _______ _______ _______ _______ _______ _______ _______ _______ _______ _______ _______ _______ _______ _______ _______ _______ _______ _______ _______ _______ _______ _______ _______ _______ _______ _______ _______ _______ _______ _______ _______ _______ _______ _______ _______ _______ _______ _______ _______ _______ _______ _______ _______ _______ _______ _______ _______ _______ _______ _______ _______ _______ _______ _______ _______ _______ _______ _______ _______ _______ _______ _______ _______ _______ _______ _______ _______ _______ _______ _______ _______ _______ _______ _______ _______ _______ _______ _______ _______ _______ _______ _______ _______ _______ _______ _______ _______ _______ _______ _______ _______ _______ _______ _______ _______ _______ _______ _______ _______ _______ _______ _______ _______ _______ _______ _______ _______ _______ _______ _______ _______ _______ _______ _______ _______ _______ _______ _______ _______ _______ _______ _______ _______ _______ _______ _______ _______ _______ _______ _______ _______ _______ _______ _______ _______ _______ _______ _______ _______ _______ _______ _______ _______ _______ _______ _______ _______ _______ _______ _______ _______ _______ _______ _______ _______ _______ _______ _______ _______ _______ _______ _______ _______ _______ _______ _______ _______ _______ ________ ________ ________ ________ ________ ________ _________ __________ _________ _________ _________ _________ _________ _________ _______ ________ ________ ________ ________ ________ ________ ________ ________ _______ ________ ________ ________ _______ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ _______ ________ ________ _______ ________ ________ ________ ________ ________ ________ ________ _______ ________ ________ ________ ________ ________ _______ _______ _______ _______ _______ _______ _______ _______ _______ _______ _______ _______ _______ _______ _______ _______ _______ _______ _______ _______ _______ _______ _______ _______ _______ _______ _______ _______ _______ _______ _______ _______ _______ Min -188.62 -188.56 -188.54 -188.57 -188.64 -188.71 -195.71 -194.74 -193.7 -192.65 -191.57 -190.47 -190.5 -190.94 -191.43 -191.96 -192.54 -193.14 -193.76 -194.42 -195.79 -194.62 -193.45 -192.27 -191.06 -189.84 -188.62 -187.4 -186.26 -187.25 -188.3 -189.38 -190.51 -191.69 -194.75 -194.17 -192.27 -191.02 -189.76 -188.52 -187.28 -186.35 -187.89 -189.44 -191.03 -192.65 -194.31 -194.68 -193.42 -192.15 -190.9 -189.65 -189.03 -190.91 -192.81 -194.74 -183.3 -182.98 -185.03 -187.11 -193.67 -192.37 -193.45 -189.6 -188.22 -186.84 -186.6 -188.85 -191.14 -193.43 -180.39 -193.91 -192.5 -191.09 -189.73 -191.97 -194.2 -185.49 -184.11 -186.28 -188.47 -194.3 -192.86 -191.44 -190.03 -188.63 -187.19 -188.67 -190.59 -192.48 -194.31 -194.25 -192.87 -191.5 -190.08 -188.68 -190.14 -191.63 -193.06 -194.44 -182.04 -182.61 -193.91 -192.72 -191.3 -190.26 -189.24 -188.55 -189.17 -189.71 -190.15 -190.53 -190.85 -191.11 -191.31 -191.43 -191.47 -194.67 -194.09 -193.54 -193.04 -192.57 -192.15 -191.72 -191.34 -191 -190.7 -190.44 -190.2 -189.96 -189.77 -189.62 -189.51 -189.46 -189.47 -189.52 -189.62 -195.72 -194.85 -193.96 -193.02 -192.06 -191.13 -191.42 -191.75 -192.12 -192.53 -192.98 -193.47 -193.96 -194.49 -195.06 -195.86 -194.8 -193.77 -192.76 -191.75 -190.74 -189.73 -188.73 -187.73 -187.36 -188.35 -189.37 -190.44 -191.54 -192.67 -195.16 -194.81 -195.93 -192.21 -191.19 -190.18 -189.18 -188.18 -189.05 -190.38 -191.74 -193.14 -194.56 -195.98 -195.22 -194.23 -193.23 -192.25 -191.26 -191.41 -193.08 -194.76 -187.37 -186.4 -186.51 -188.34 -190.16 -195.14 -194.09 -195.47 -191.96 -190.9 -189.84 -190.22 -192.05 -193.88 -195.7 -185.47 -195.37 -194.21 -193.05 -192.85 -194.67 -189.38 -188.16 -188.21 -190.06 -191.88 -195.38 -195.42 -192.89 -191.66 -190.44 -190.43 -192.11 -193.73 -195.33 -185.11 -194.8 -193.59 -192.35 -191.12 -192.24 -193.5 -194.72 -195.88 -185.21 -195.96 -194.79 -193.61 -192.45 -191.44 -190.39 -190.78 -191.34 -191.84 -192.28 -192.66 -192.98 -193.25 -193.41 -195.59 -194.89 -194.24 -193.63 -193.39 -193.23 -192.97 -192.66 -192.3 -191.89 -191.44 -190.89 -190.21 -189.47 -188.7 -188.32 -188.09 -187.9 -187.76 -187.67 -187.63 -187.64 -187.72 -187.85 -194.99 -193.9 -192.79 -191.66 -190.51 -189.47 -189.88 -190.32 -190.79 -191.31 -191.87 -192.49 -193.15 -193.86 -194.64 -193.34 -192.04 -190.74 -189.43 -188.06 -186.68 -185.31 -184.86 -185.98 -187.16 -188.38 -189.65 -190.97 -193.41 -193.74 -190.78 -189.48 -188.18 -186.89 -185.62 -185.66 -187.35 -189.03 -190.75 -192.49 -194.23 -193.59 -192.32 -191.05 -189.79 -188.54 -189.02 -190.99 -193 -183.45 -182.13 -183.03 -185.19 -194.34 -193.01 -191.78 -194.01 -188.97 -187.67 -186.36 -187.42 -189.75 -192.08 -194.38 -181.55 -193.66 -192.32 -190.99 -190.92 -193.15 -187.05 -185.76 -185.58 -187.77 -189.89 -194.38 -194.05 -191.72 -190.41 -189.06 -188.35 -190.24 -192.09 -193.9 -182.47 -194.62 -193.33 -192.03 -190.75 -190.32 -191.77 -193.15 -194.48 -184.68 -183.7 -195.59 -194.47 -193.38 -192.17 -191.22 -190.28 -189.9 -190.42 -190.87 -191.25 -191.55 -191.78 -191.96 -192.08 -192.15 -195.81 -195.23 -194.68 -194.15 -193.62 -193.13 -192.68 -192.27 -191.89 -191.56 -191.27 -191.01 -190.8 -190.63 -190.49 -190.35 -190.25 Max 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 Mean -37.011 -36.584 -36.176 -35.781 -35.391 -35.006 -36.448 -36.078 -35.719 -35.369 -35.029 -34.7 -34.386 -34.084 -33.793 -33.521 -33.266 -33.024 -32.8 -32.595 -34.224 -32.227 -32.059 -31.908 -31.773 -31.657 -31.562 -31.481 -31.418 -31.369 -31.337 -31.319 -31.313 -31.321 -33.154 -33.169 -31.372 -31.396 -31.433 -31.485 -31.554 -31.64 -31.744 -31.867 -32.008 -32.174 -32.362 -32.561 -32.766 -32.994 -33.247 -33.522 -33.817 -34.131 -34.461 -34.806 -33.33 -33.688 -34.06 -34.443 -36.615 -36.99 -37.374 -35.97 -36.342 -36.728 -37.122 -37.515 -37.913 -38.317 -36.925 -39.106 -39.493 -39.888 -40.283 -40.679 -41.079 -39.698 -40.077 -40.458 -40.838 -42.944 -43.286 -41.884 -42.19 -42.482 -42.762 -43.029 -43.281 -43.516 -43.734 -43.911 -44.059 -44.187 -44.295 -44.383 -44.45 -44.498 -44.527 -44.539 -42.771 -42.73 -44.409 -44.319 -44.231 -44.096 -43.949 -43.781 -43.598 -43.399 -43.185 -42.954 -42.714 -42.458 -42.188 -41.902 -41.597 -43.023 -42.688 -42.336 -41.972 -41.592 -41.203 -40.8 -40.381 -39.952 -39.516 -39.072 -38.623 -38.169 -37.717 -37.265 -36.817 -36.374 -35.942 -35.525 -35.122 -36.54 -36.16 -35.8 -35.456 -35.128 -34.817 -34.524 -34.249 -33.993 -33.75 -33.522 -33.308 -33.109 -32.924 -32.75 -34.405 -32.428 -32.278 -32.134 -32.005 -31.891 -31.788 -31.693 -31.613 -31.549 -31.502 -31.47 -31.454 -31.459 -31.482 -33.343 -33.394 -33.459 -31.709 -31.803 -31.914 -32.045 -32.194 -32.359 -32.542 -32.74 -32.953 -33.182 -33.426 -33.661 -33.905 -34.166 -34.443 -34.731 -35.033 -35.35 -35.683 -34.206 -34.549 -34.909 -35.281 -35.671 -37.857 -38.25 -38.654 -37.272 -37.68 -38.093 -38.499 -38.913 -39.329 -39.754 -38.374 -40.562 -40.963 -41.372 -41.78 -42.18 -40.808 -41.195 -41.579 -41.952 -42.315 -44.408 -44.742 -43.319 -43.616 -43.898 -44.166 -44.418 -44.652 -44.873 -43.315 -45.223 -45.354 -45.463 -45.559 -45.633 -45.684 -45.711 -45.717 -43.942 -45.64 -45.554 -45.445 -45.315 -45.164 -44.979 -44.771 -44.541 -44.294 -44.027 -43.742 -43.438 -43.118 -42.781 -44.164 -43.787 -43.402 -43.005 -42.595 -42.173 -41.741 -41.302 -40.857 -40.407 -39.954 -39.496 -39.035 -38.574 -38.116 -37.661 -37.208 -36.761 -36.322 -35.892 -35.468 -35.055 -34.649 -34.254 -35.681 -35.301 -34.93 -34.572 -34.23 -33.902 -33.588 -33.291 -33.008 -32.738 -32.485 -32.251 -32.033 -31.829 -33.453 -31.449 -31.275 -31.119 -30.98 -30.864 -30.758 -30.672 -30.619 -30.584 -30.556 -30.558 -30.591 -30.581 -32.419 -32.463 -30.689 -30.74 -30.806 -30.896 -31.001 -31.128 -31.277 -31.432 -31.615 -31.822 -32.043 -32.259 -32.491 -32.743 -33.021 -33.319 -33.636 -33.977 -34.336 -32.889 -33.265 -33.653 -34.051 -36.264 -36.672 -37.08 -37.504 -36.132 -36.545 -36.969 -37.399 -37.832 -38.274 -38.719 -37.356 -39.559 -39.976 -40.396 -40.815 -41.232 -39.887 -40.285 -40.682 -41.074 -41.463 -43.574 -43.932 -42.537 -42.854 -43.159 -43.451 -43.727 -43.991 -44.239 -42.712 -44.651 -44.812 -44.954 -45.076 -45.178 -45.262 -45.321 -45.358 -43.627 -43.608 -45.314 -45.238 -45.141 -45.025 -44.874 -44.699 -44.5 -44.289 -44.06 -43.811 -43.533 -43.239 -42.931 -42.613 -42.28 -43.676 -43.306 -42.93 -42.548 -42.156 -41.751 -41.34 -40.929 -40.517 -40.098 -39.68 -39.261 -38.849 -38.435 -38.025 -37.617 -37.209 RMS 72.094 71.749 71.411 71.083 70.77 70.469 72.8 72.506 72.222 71.948 71.688 71.442 71.207 70.986 70.778 70.58 70.395 70.22 70.055 69.902 72.379 69.594 69.42 69.252 69.096 68.951 68.82 68.704 68.614 68.537 68.485 68.452 68.443 68.454 71.129 71.143 68.508 68.519 68.555 68.615 68.694 68.793 68.912 69.045 69.192 69.357 69.538 69.7 69.828 69.973 70.135 70.308 70.493 70.695 70.91 71.141 68.666 68.89 69.143 69.412 72.284 72.534 72.801 70.48 70.727 70.995 71.284 71.587 71.9 72.226 69.904 72.807 73.075 73.356 73.638 73.924 74.219 71.932 72.188 72.46 72.737 75.491 75.718 73.473 73.654 73.835 74.019 74.207 74.394 74.578 74.761 74.885 74.973 75.06 75.141 75.217 75.284 75.344 75.399 75.447 72.928 72.92 75.401 75.34 75.277 75.19 75.091 74.975 74.846 74.707 74.555 74.393 74.219 74.035 73.842 73.633 73.41 75.666 75.422 75.173 74.919 74.66 74.396 74.125 73.855 73.584 73.313 73.043 72.774 72.506 72.237 71.968 71.701 71.441 71.188 70.939 70.698 73.071 72.823 72.578 72.337 72.102 71.876 71.655 71.439 71.233 71.033 70.843 70.664 70.496 70.338 70.194 72.669 69.908 69.767 69.635 69.517 69.417 69.333 69.265 69.21 69.173 69.151 69.142 69.152 69.186 69.235 71.918 71.966 72.027 69.435 69.497 69.577 69.671 69.784 69.914 70.064 70.228 70.407 70.608 70.821 70.985 71.161 71.35 71.558 71.784 72.021 72.274 72.547 70.192 70.461 70.75 71.059 71.384 74.257 74.571 74.898 72.67 72.965 73.278 73.603 73.936 74.259 74.586 72.33 75.178 75.46 75.75 76.046 76.354 74.177 74.452 74.733 75.017 75.306 77.996 78.238 76.048 76.249 76.445 76.636 76.823 76.998 77.169 74.834 77.392 77.458 77.514 77.566 77.608 77.639 77.657 77.665 75.17 77.586 77.493 77.386 77.273 77.138 76.989 76.825 76.65 76.465 76.266 76.054 75.831 75.599 75.349 77.509 77.221 76.929 76.629 76.321 75.997 75.664 75.327 74.987 74.642 74.296 73.942 73.584 73.225 72.869 72.515 72.161 71.81 71.463 71.125 70.794 70.466 70.143 69.831 72.159 71.841 71.53 71.228 70.937 70.658 70.39 70.138 69.894 69.661 69.443 69.241 69.051 68.876 71.344 68.496 68.297 68.115 67.95 67.801 67.674 67.568 67.499 67.45 67.426 67.423 67.442 67.445 70.136 70.16 67.507 67.536 67.588 67.662 67.772 67.904 68.057 68.229 68.421 68.631 68.861 69.027 69.188 69.366 69.567 69.781 70.01 70.263 70.533 68.123 68.382 68.656 68.951 71.899 72.184 72.482 72.803 70.513 70.81 71.133 71.475 71.833 72.213 72.603 70.332 73.265 73.587 73.922 74.265 74.614 72.437 72.754 73.079 73.408 73.737 76.499 76.781 74.605 74.838 75.068 75.297 75.518 75.739 75.953 73.646 76.29 76.403 76.509 76.608 76.695 76.778 76.847 76.907 74.47 74.471 76.946 76.895 76.832 76.755 76.657 76.541 76.407 76.266 76.11 75.938 75.743 75.54 75.323 75.094 74.851 77.054 76.786 76.51 76.224 75.931 75.637 75.339 75.034 74.725 74.417 74.107 73.798 73.492 73.187 72.868 72.54 72.216 AUC 1.3271 1.3339 1.3409 1.3478 1.3544 1.3609 1.3918 1.3985 1.405 1.4115 1.4179 1.4241 1.4304 1.4367 1.4429 1.4492 1.4557 1.4622 1.4687 1.4755 1.5081 1.4891 1.4959 1.5027 1.5096 1.5165 1.5237 1.5309 1.5382 1.5454 1.5527 1.56 1.5673 1.5746 1.6109 1.6178 1.5949 1.6012 1.6074 1.6137 1.6202 1.6268 1.6336 1.6406 1.6477 1.6552 1.6631 1.671 1.6786 1.6866 1.6949 1.7037 1.7126 1.7219 1.7311 1.7405 1.7166 1.7257 1.735 1.7443 1.7854 1.794 1.8024 1.7788 1.7866 1.7946 1.8027 1.8105 1.8183 1.8262 1.7997 1.8409 1.848 1.8551 1.8622 1.8691 1.8762 1.8515 1.8582 1.8649 1.8717 1.9075 1.913 1.8893 1.8942 1.8988 1.9032 1.9074 1.9114 1.915 1.9183 1.9208 1.9229 1.9247 1.9261 1.9273 1.9281 1.9288 1.9292 1.9295 1.8969 1.8963 1.9276 1.9263 1.9251 1.9234 1.9215 1.9194 1.917 1.9145 1.9117 1.9088 1.9057 1.9024 1.8989 1.895 1.8907 1.9156 1.9108 1.9056 1.9001 1.8942 1.8879 1.8813 1.8741 1.8667 1.859 1.8511 1.843 1.8345 1.826 1.8174 1.8088 1.8002 1.7918 1.7836 1.7757 1.7986 1.791 1.7837 1.7766 1.7696 1.7629 1.7562 1.7497 1.7434 1.7371 1.7309 1.7247 1.7185 1.7124 1.7063 1.7282 1.6937 1.6872 1.6804 1.6738 1.6672 1.6605 1.6538 1.6471 1.6405 1.6341 1.6276 1.6212 1.615 1.6088 1.6277 1.6211 1.6144 1.5819 1.5755 1.5691 1.5626 1.5562 1.5498 1.5434 1.537 1.5307 1.5244 1.5181 1.5111 1.5041 1.4971 1.4902 1.4832 1.4761 1.4691 1.462 1.4321 1.4249 1.4178 1.4106 1.4034 1.4154 1.4072 1.399 1.3717 1.3632 1.3545 1.3455 1.3364 1.3272 1.3178 1.2895 1.2985 1.2884 1.2784 1.2682 1.2579 1.2308 1.2204 1.21 1.1994 1.1887 1.1931 1.1817 1.1554 1.1438 1.132 1.1202 1.1081 1.0959 1.0836 1.0562 1.058 1.0448 1.0313 1.0179 1.0042 0.99038 0.97631 0.96215 0.93468 0.93382 0.91898 0.90401 0.88896 0.86585 0.85058 0.83522 0.81981 0.80433 0.78878 0.77315 0.75745 0.74168 0.72579 0.72008 0.70384 0.68761 0.67135 0.65504 0.63869 0.62227 0.60584 0.58943 0.57303 0.55666 0.54019 0.52371 0.5073 0.49094 0.47466 0.45849 0.44243 0.42647 0.41063 0.39492 0.37932 0.36382 0.34847 0.34028 0.32489 0.30961 0.29445 0.27939 0.26444 0.2496 0.23484 0.22013 0.20548 0.19086 0.17632 0.16181 0.14724 0.13593 0.11823 0.10375 0.089302 0.074967 0.060681 0.046414 0.032391 0.018353 0.0044501 -0.0092272 -0.022865 -0.036396 -0.049784 -0.063592 -0.077045 -0.089225 -0.1022 -0.11521 -0.12812 -0.14097 -0.15392 -0.16696 -0.17999 -0.19316 -0.20648 -0.2199 -0.23337 -0.24702 -0.26076 -0.2747 -0.28882 -0.30311 -0.31757 -0.33223 -0.34048 -0.35508 -0.36982 -0.38465 -0.40688 -0.42205 -0.43719 -0.45243 -0.45964 -0.47462 -0.48964 -0.50469 -0.51971 -0.53479 -0.54987 -0.55463 -0.57948 -0.59426 -0.60904 -0.62379 -0.63857 -0.64288 -0.65743 -0.67201 -0.68663 -0.70127 -0.72653 -0.74117 -0.7448 -0.75913 -0.77342 -0.7877 -0.80196 -0.81625 -0.83048 -0.83114 -0.85848 -0.87222 -0.88589 -0.8995 -0.91302 -0.92651 -0.93986 -0.95309 -0.95128 -0.964 -0.9918 -1.0042 -1.0166 -1.0363 -1.0483 -1.06 -1.0715 -1.083 -1.0944 -1.1056 -1.1164 -1.1269 -1.1374 -1.1477 -1.1578 -1.185 -1.1949 -1.2047 -1.2143 -1.2237 -1.2329 -1.242 -1.2509 -1.2597 -1.2682 -1.2767 -1.2851 -1.2936 -1.3016 -1.3096 -1.3174 -1.3252

figure

hs1 = scatter(x, y, '.', 'DisplayName','Data');

hold on

hp1 = plot(x(kb), y(kb), '-r', 'LineWidth',3, 'DisplayName','Boundary');

for k1 = 1:size(xr,2)

hp2 = plot(xr(:,k1), yr(:,k1), '-c', 'LineWidth',0.5, 'DisplayName','Regression Lines');

end

for k1 = 1:10:numel(xp)

if (min(yp{k1} >= yl(1)) & (max(yp{k1} <= yl(2))))

hp3 = plot(xp{k1}, yp{k1}, '-r', 'DisplayName','Selected Linear Regression Lines Used To Compute ‘Reesult’');

end

hp4 = plot(cx, cy, 'pg', 'MarkerSize',15, 'MarkerFaceColor','g', 'DisplayName','Centroid');

hold off

grid

axis('equal')

xlabel('X')

ylabel('Y')

title('Top Surface At 50s Showing Regression Lines')

legend([hs1(1) hp1 hp2(1) hp3(1) hp4], 'Location','northoutside')

EDIT — (9 Sep 2024 at 13:07)

Added .‘Bref’

.

Rohit 2024-9-9

@Star Strider thank you for your efforts.

Star Strider 2024-9-9

As always, my pleasure!

This is an interesting problem!

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Answer 2

Walter Roberson 2024-9-7

0
链接

此回答的直接链接

https://ww2.mathworks.cn/matlabcentral/answers/2151039-best-fit-line-for-scatter-data-along-y-x-line#answer_1512659

在 MATLAB Online 中打开

m = (y2 - y1) ./ (x2 - x1);
b = y1 - x1 .* m;
LD1= load('uu.mat');
uu = LD1.uu;
LD2 = load('PntP.mat');
x = LD2.PntP(:,1);
y = LD2.PntP(:,2);
F = scatteredInterpolant(x, y, uu, 'linear', 'none');
xq = linspace(min(x), max(x));
yq = m * xq + b;
interpolated_data = F(xq, yq);
plot3(xq, yq, interpolated_data)

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Rohit 2024-9-9

If we replace F = scatteredInterpolant(x, y, uu, 'linear', 'none'); by

F = scatteredInterpolant(x, y, uu(:,50), 'linear', 'none'); Than it will give the temperature on line at time 50 sec. it will work but my concern is that line should paas through the center (0.02,0.02) and both point (x1,y1) and (x2,y2) should satisfy the line. suppose I choose first cordinate like (0.03,0.022). How i will choose the second coordinate so that it satisfy the equation of line. After getting the line it may be possible some coordinates of the given PntP not lies in line so we will use interpolation. Now on this equation of line each coordinate i have to find the temperature also.

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Best fit line for scatter data along y=x line

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Best fit line for scatter data along y=x line

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