remove inf in quiver

I want quiver(X,Y,u,v), but there's inf entries in u and v at positions x=y. I'm looking for the smartest way to skip these positions with inf u and v and finish the quiver.

x=-5:0.1:5;
y=-5:0.1:5;
[X,Y]=meshgrid(x,y);

[Dyuman Joshi]

u and v aren't defined in the code.

Answer 1

[Star Strider]

Without having ‘u’ and ‘v’ to work with, perhaps something like this using fillmissing (or fillmissing2) —

x=-5:0.1:5;
y=-5:0.1:5;
[X,Y]=meshgrid(x,y);
u = randn(size(X));                                         % Create 'u'
ix = sub2ind(size(u), 1:size(u,1), 1:size(u,2));            % Linear Inmdex To Create 'u' With Diagnonal 'Inf'
u(ix) = Inf

u = 101x101

Inf -1.4193 1.0497 -0.1100 0.2181 -2.3272 -0.1111 0.6233 -0.4594 0.1195 0.9566 -1.9244 -1.4146 -0.4970 0.0888 1.9743 0.4177 -0.6746 1.0932 1.6275 -0.3020 2.6394 0.0664 -0.9736 0.5496 0.9406 0.7607 -1.1810 0.1057 0.3238 -0.2040 Inf 1.4676 -0.4032 -0.8914 0.6347 0.2933 -0.7958 -0.5473 0.3441 -1.0796 -0.2790 -0.8720 -0.1744 1.0271 0.2945 -0.6075 -0.3061 0.9589 -0.3907 -0.1497 -0.5693 1.9353 -0.9865 1.5239 0.9541 1.2692 0.5700 0.6326 -0.2809 0.4440 0.1059 Inf -0.7628 -1.9011 0.4484 -0.6286 -0.0539 -0.9827 -0.1590 -1.1202 0.9885 -0.1086 -0.2550 0.5774 -0.3649 0.9951 0.3727 0.7720 0.5775 1.4696 -0.2848 0.2413 -0.7008 -1.0953 -1.5702 -0.5243 -0.0585 -2.2009 -2.7097 -1.1253 -0.7258 -0.8600 Inf -0.7818 0.0430 1.3831 0.4638 -1.8246 -0.5975 1.1344 -2.0120 0.3844 -1.4048 -0.7412 -0.7979 -0.3750 1.3553 -0.5779 0.0419 0.5819 1.2048 -0.7356 -0.4025 -0.4867 -2.7108 -0.5462 -0.5905 0.7864 -1.9629 0.7305 1.5534 0.0548 0.6495 Inf 0.9854 0.1087 0.1832 0.1335 2.4604 -0.5619 2.5730 0.3904 1.0272 -0.4547 1.0581 0.3803 0.3861 -0.6168 1.0308 -1.0501 1.1463 -0.4087 -0.2244 -1.0732 1.3175 -0.1320 0.2752 0.8637 -0.0700 1.5087 0.2137 -0.5525 0.6823 1.3532 Inf -0.9245 0.0744 -0.7550 -1.0429 -0.4523 0.1324 0.3328 0.8958 -0.8247 0.0939 -0.9691 -0.3163 0.4888 -0.2547 2.0617 -1.2665 -1.3080 -0.2838 0.4716 0.0971 1.3410 0.4530 -0.5502 -0.3113 -0.7899 -1.3514 1.8762 1.4511 -0.6696 0.6343 Inf 0.5961 0.5598 1.2437 0.3595 -0.3161 0.0932 1.3476 -1.0957 0.1353 0.3317 -1.3635 0.9710 1.6089 -1.9098 0.8884 0.3416 0.1062 0.8726 1.1483 -0.3553 -0.5328 -0.2019 -0.8153 0.7720 0.5451 0.7223 -1.8662 -0.6066 -0.0011 1.0699 Inf -0.3192 1.2113 -0.9188 -0.0333 -0.5733 0.0934 -0.1295 1.4446 -1.1444 1.1519 -0.9562 -0.6240 -0.0261 0.5102 -1.5159 -0.8370 0.7812 1.1397 0.1271 0.5030 -0.8183 1.9334 -1.0089 -1.5652 0.4866 -1.7667 0.1231 0.1948 -1.0970 -0.6456 Inf 0.6980 -1.0084 0.9558 -1.3121 -0.8189 -1.0246 -0.6636 -1.7755 0.1567 0.6217 -0.1159 0.8454 -0.0712 1.8066 -0.7937 0.6682 -0.0229 -1.0841 1.5203 -1.2465 -1.5232 0.1843 1.4161 1.2349 -1.0478 -0.4456 0.2213 0.2403 -0.9400 0.1554 Inf -0.4139 -1.0749 -0.7384 -1.5659 1.6817 -1.2396 -0.7250 0.3642 -0.1862 0.4102 1.0891 -0.9765 0.1297 1.0545 -0.4711 -0.9109 -1.2133 0.7924 -0.2372 -0.9673

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u(isinf(u)) = NaN                                           % Change 'Inf' To 'NaN'

u = 101x101

NaN -1.4193 1.0497 -0.1100 0.2181 -2.3272 -0.1111 0.6233 -0.4594 0.1195 0.9566 -1.9244 -1.4146 -0.4970 0.0888 1.9743 0.4177 -0.6746 1.0932 1.6275 -0.3020 2.6394 0.0664 -0.9736 0.5496 0.9406 0.7607 -1.1810 0.1057 0.3238 -0.2040 NaN 1.4676 -0.4032 -0.8914 0.6347 0.2933 -0.7958 -0.5473 0.3441 -1.0796 -0.2790 -0.8720 -0.1744 1.0271 0.2945 -0.6075 -0.3061 0.9589 -0.3907 -0.1497 -0.5693 1.9353 -0.9865 1.5239 0.9541 1.2692 0.5700 0.6326 -0.2809 0.4440 0.1059 NaN -0.7628 -1.9011 0.4484 -0.6286 -0.0539 -0.9827 -0.1590 -1.1202 0.9885 -0.1086 -0.2550 0.5774 -0.3649 0.9951 0.3727 0.7720 0.5775 1.4696 -0.2848 0.2413 -0.7008 -1.0953 -1.5702 -0.5243 -0.0585 -2.2009 -2.7097 -1.1253 -0.7258 -0.8600 NaN -0.7818 0.0430 1.3831 0.4638 -1.8246 -0.5975 1.1344 -2.0120 0.3844 -1.4048 -0.7412 -0.7979 -0.3750 1.3553 -0.5779 0.0419 0.5819 1.2048 -0.7356 -0.4025 -0.4867 -2.7108 -0.5462 -0.5905 0.7864 -1.9629 0.7305 1.5534 0.0548 0.6495 NaN 0.9854 0.1087 0.1832 0.1335 2.4604 -0.5619 2.5730 0.3904 1.0272 -0.4547 1.0581 0.3803 0.3861 -0.6168 1.0308 -1.0501 1.1463 -0.4087 -0.2244 -1.0732 1.3175 -0.1320 0.2752 0.8637 -0.0700 1.5087 0.2137 -0.5525 0.6823 1.3532 NaN -0.9245 0.0744 -0.7550 -1.0429 -0.4523 0.1324 0.3328 0.8958 -0.8247 0.0939 -0.9691 -0.3163 0.4888 -0.2547 2.0617 -1.2665 -1.3080 -0.2838 0.4716 0.0971 1.3410 0.4530 -0.5502 -0.3113 -0.7899 -1.3514 1.8762 1.4511 -0.6696 0.6343 NaN 0.5961 0.5598 1.2437 0.3595 -0.3161 0.0932 1.3476 -1.0957 0.1353 0.3317 -1.3635 0.9710 1.6089 -1.9098 0.8884 0.3416 0.1062 0.8726 1.1483 -0.3553 -0.5328 -0.2019 -0.8153 0.7720 0.5451 0.7223 -1.8662 -0.6066 -0.0011 1.0699 NaN -0.3192 1.2113 -0.9188 -0.0333 -0.5733 0.0934 -0.1295 1.4446 -1.1444 1.1519 -0.9562 -0.6240 -0.0261 0.5102 -1.5159 -0.8370 0.7812 1.1397 0.1271 0.5030 -0.8183 1.9334 -1.0089 -1.5652 0.4866 -1.7667 0.1231 0.1948 -1.0970 -0.6456 NaN 0.6980 -1.0084 0.9558 -1.3121 -0.8189 -1.0246 -0.6636 -1.7755 0.1567 0.6217 -0.1159 0.8454 -0.0712 1.8066 -0.7937 0.6682 -0.0229 -1.0841 1.5203 -1.2465 -1.5232 0.1843 1.4161 1.2349 -1.0478 -0.4456 0.2213 0.2403 -0.9400 0.1554 NaN -0.4139 -1.0749 -0.7384 -1.5659 1.6817 -1.2396 -0.7250 0.3642 -0.1862 0.4102 1.0891 -0.9765 0.1297 1.0545 -0.4711 -0.9109 -1.2133 0.7924 -0.2372 -0.9673

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u = fillmissing(u,'linear')                                 % Use 'fillmissing'

u = 101x101

-0.8520 -1.4193 1.0497 -0.1100 0.2181 -2.3272 -0.1111 0.6233 -0.4594 0.1195 0.9566 -1.9244 -1.4146 -0.4970 0.0888 1.9743 0.4177 -0.6746 1.0932 1.6275 -0.3020 2.6394 0.0664 -0.9736 0.5496 0.9406 0.7607 -1.1810 0.1057 0.3238 -0.2040 -0.6567 1.4676 -0.4032 -0.8914 0.6347 0.2933 -0.7958 -0.5473 0.3441 -1.0796 -0.2790 -0.8720 -0.1744 1.0271 0.2945 -0.6075 -0.3061 0.9589 -0.3907 -0.1497 -0.5693 1.9353 -0.9865 1.5239 0.9541 1.2692 0.5700 0.6326 -0.2809 0.4440 0.1059 0.3038 -0.7628 -1.9011 0.4484 -0.6286 -0.0539 -0.9827 -0.1590 -1.1202 0.9885 -0.1086 -0.2550 0.5774 -0.3649 0.9951 0.3727 0.7720 0.5775 1.4696 -0.2848 0.2413 -0.7008 -1.0953 -1.5702 -0.5243 -0.0585 -2.2009 -2.7097 -1.1253 -0.7258 -0.8600 -0.0567 -0.7818 0.0430 1.3831 0.4638 -1.8246 -0.5975 1.1344 -2.0120 0.3844 -1.4048 -0.7412 -0.7979 -0.3750 1.3553 -0.5779 0.0419 0.5819 1.2048 -0.7356 -0.4025 -0.4867 -2.7108 -0.5462 -0.5905 0.7864 -1.9629 0.7305 1.5534 0.0548 0.6495 0.2857 0.9854 0.1087 0.1832 0.1335 2.4604 -0.5619 2.5730 0.3904 1.0272 -0.4547 1.0581 0.3803 0.3861 -0.6168 1.0308 -1.0501 1.1463 -0.4087 -0.2244 -1.0732 1.3175 -0.1320 0.2752 0.8637 -0.0700 1.5087 0.2137 -0.5525 0.6823 1.3532 0.8098 -0.9245 0.0744 -0.7550 -1.0429 -0.4523 0.1324 0.3328 0.8958 -0.8247 0.0939 -0.9691 -0.3163 0.4888 -0.2547 2.0617 -1.2665 -1.3080 -0.2838 0.4716 0.0971 1.3410 0.4530 -0.5502 -0.3113 -0.7899 -1.3514 1.8762 1.4511 -0.6696 0.6343 0.0727 0.5961 0.5598 1.2437 0.3595 -0.3161 0.0932 1.3476 -1.0957 0.1353 0.3317 -1.3635 0.9710 1.6089 -1.9098 0.8884 0.3416 0.1062 0.8726 1.1483 -0.3553 -0.5328 -0.2019 -0.8153 0.7720 0.5451 0.7223 -1.8662 -0.6066 -0.0011 1.0699 -0.0247 -0.3192 1.2113 -0.9188 -0.0333 -0.5733 0.0934 -0.1295 1.4446 -1.1444 1.1519 -0.9562 -0.6240 -0.0261 0.5102 -1.5159 -0.8370 0.7812 1.1397 0.1271 0.5030 -0.8183 1.9334 -1.0089 -1.5652 0.4866 -1.7667 0.1231 0.1948 -1.0970 -0.6456 -0.0819 0.6980 -1.0084 0.9558 -1.3121 -0.8189 -1.0246 -0.6636 -1.7755 0.1567 0.6217 -0.1159 0.8454 -0.0712 1.8066 -0.7937 0.6682 -0.0229 -1.0841 1.5203 -1.2465 -1.5232 0.1843 1.4161 1.2349 -1.0478 -0.4456 0.2213 0.2403 -0.9400 0.1554 0.8220 -0.4139 -1.0749 -0.7384 -1.5659 1.6817 -1.2396 -0.7250 0.3642 -0.1862 0.4102 1.0891 -0.9765 0.1297 1.0545 -0.4711 -0.9109 -1.2133 0.7924 -0.2372 -0.9673

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Use whatever interpolation method you want with fillmissing. There are several options.

.

[feynman feynman]

That's wonderful thank you. Is it possible to just leave those inf blank where quiver skips? This code is to show a vector field, but replacing those inf with some random numbers varies the original vector field. I prefer showing the original vector field without changing it and skipping the inf.

[Star Strider]

Thank you!

The problem with leaving them blank is that reduces the matrix by 1 in the row size. That makes plotting them with the original (X,Y) matrices inpossible, unless you also delete the corresponding diagonal elements in the (X,Y) matrices.

One way to do that is to use a version of my original code for all the matrices —

x=-5:0.1:5;
y=-5:0.1:5;
[X,Y]=meshgrid(x,y)

X = 101x101

-5.0000 -4.9000 -4.8000 -4.7000 -4.6000 -4.5000 -4.4000 -4.3000 -4.2000 -4.1000 -4.0000 -3.9000 -3.8000 -3.7000 -3.6000 -3.5000 -3.4000 -3.3000 -3.2000 -3.1000 -3.0000 -2.9000 -2.8000 -2.7000 -2.6000 -2.5000 -2.4000 -2.3000 -2.2000 -2.1000 -5.0000 -4.9000 -4.8000 -4.7000 -4.6000 -4.5000 -4.4000 -4.3000 -4.2000 -4.1000 -4.0000 -3.9000 -3.8000 -3.7000 -3.6000 -3.5000 -3.4000 -3.3000 -3.2000 -3.1000 -3.0000 -2.9000 -2.8000 -2.7000 -2.6000 -2.5000 -2.4000 -2.3000 -2.2000 -2.1000 -5.0000 -4.9000 -4.8000 -4.7000 -4.6000 -4.5000 -4.4000 -4.3000 -4.2000 -4.1000 -4.0000 -3.9000 -3.8000 -3.7000 -3.6000 -3.5000 -3.4000 -3.3000 -3.2000 -3.1000 -3.0000 -2.9000 -2.8000 -2.7000 -2.6000 -2.5000 -2.4000 -2.3000 -2.2000 -2.1000 -5.0000 -4.9000 -4.8000 -4.7000 -4.6000 -4.5000 -4.4000 -4.3000 -4.2000 -4.1000 -4.0000 -3.9000 -3.8000 -3.7000 -3.6000 -3.5000 -3.4000 -3.3000 -3.2000 -3.1000 -3.0000 -2.9000 -2.8000 -2.7000 -2.6000 -2.5000 -2.4000 -2.3000 -2.2000 -2.1000 -5.0000 -4.9000 -4.8000 -4.7000 -4.6000 -4.5000 -4.4000 -4.3000 -4.2000 -4.1000 -4.0000 -3.9000 -3.8000 -3.7000 -3.6000 -3.5000 -3.4000 -3.3000 -3.2000 -3.1000 -3.0000 -2.9000 -2.8000 -2.7000 -2.6000 -2.5000 -2.4000 -2.3000 -2.2000 -2.1000 -5.0000 -4.9000 -4.8000 -4.7000 -4.6000 -4.5000 -4.4000 -4.3000 -4.2000 -4.1000 -4.0000 -3.9000 -3.8000 -3.7000 -3.6000 -3.5000 -3.4000 -3.3000 -3.2000 -3.1000 -3.0000 -2.9000 -2.8000 -2.7000 -2.6000 -2.5000 -2.4000 -2.3000 -2.2000 -2.1000 -5.0000 -4.9000 -4.8000 -4.7000 -4.6000 -4.5000 -4.4000 -4.3000 -4.2000 -4.1000 -4.0000 -3.9000 -3.8000 -3.7000 -3.6000 -3.5000 -3.4000 -3.3000 -3.2000 -3.1000 -3.0000 -2.9000 -2.8000 -2.7000 -2.6000 -2.5000 -2.4000 -2.3000 -2.2000 -2.1000 -5.0000 -4.9000 -4.8000 -4.7000 -4.6000 -4.5000 -4.4000 -4.3000 -4.2000 -4.1000 -4.0000 -3.9000 -3.8000 -3.7000 -3.6000 -3.5000 -3.4000 -3.3000 -3.2000 -3.1000 -3.0000 -2.9000 -2.8000 -2.7000 -2.6000 -2.5000 -2.4000 -2.3000 -2.2000 -2.1000 -5.0000 -4.9000 -4.8000 -4.7000 -4.6000 -4.5000 -4.4000 -4.3000 -4.2000 -4.1000 -4.0000 -3.9000 -3.8000 -3.7000 -3.6000 -3.5000 -3.4000 -3.3000 -3.2000 -3.1000 -3.0000 -2.9000 -2.8000 -2.7000 -2.6000 -2.5000 -2.4000 -2.3000 -2.2000 -2.1000 -5.0000 -4.9000 -4.8000 -4.7000 -4.6000 -4.5000 -4.4000 -4.3000 -4.2000 -4.1000 -4.0000 -3.9000 -3.8000 -3.7000 -3.6000 -3.5000 -3.4000 -3.3000 -3.2000 -3.1000 -3.0000 -2.9000 -2.8000 -2.7000 -2.6000 -2.5000 -2.4000 -2.3000 -2.2000 -2.1000

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Y = 101x101

-5.0000 -5.0000 -5.0000 -5.0000 -5.0000 -5.0000 -5.0000 -5.0000 -5.0000 -5.0000 -5.0000 -5.0000 -5.0000 -5.0000 -5.0000 -5.0000 -5.0000 -5.0000 -5.0000 -5.0000 -5.0000 -5.0000 -5.0000 -5.0000 -5.0000 -5.0000 -5.0000 -5.0000 -5.0000 -5.0000 -4.9000 -4.9000 -4.9000 -4.9000 -4.9000 -4.9000 -4.9000 -4.9000 -4.9000 -4.9000 -4.9000 -4.9000 -4.9000 -4.9000 -4.9000 -4.9000 -4.9000 -4.9000 -4.9000 -4.9000 -4.9000 -4.9000 -4.9000 -4.9000 -4.9000 -4.9000 -4.9000 -4.9000 -4.9000 -4.9000 -4.8000 -4.8000 -4.8000 -4.8000 -4.8000 -4.8000 -4.8000 -4.8000 -4.8000 -4.8000 -4.8000 -4.8000 -4.8000 -4.8000 -4.8000 -4.8000 -4.8000 -4.8000 -4.8000 -4.8000 -4.8000 -4.8000 -4.8000 -4.8000 -4.8000 -4.8000 -4.8000 -4.8000 -4.8000 -4.8000 -4.7000 -4.7000 -4.7000 -4.7000 -4.7000 -4.7000 -4.7000 -4.7000 -4.7000 -4.7000 -4.7000 -4.7000 -4.7000 -4.7000 -4.7000 -4.7000 -4.7000 -4.7000 -4.7000 -4.7000 -4.7000 -4.7000 -4.7000 -4.7000 -4.7000 -4.7000 -4.7000 -4.7000 -4.7000 -4.7000 -4.6000 -4.6000 -4.6000 -4.6000 -4.6000 -4.6000 -4.6000 -4.6000 -4.6000 -4.6000 -4.6000 -4.6000 -4.6000 -4.6000 -4.6000 -4.6000 -4.6000 -4.6000 -4.6000 -4.6000 -4.6000 -4.6000 -4.6000 -4.6000 -4.6000 -4.6000 -4.6000 -4.6000 -4.6000 -4.6000 -4.5000 -4.5000 -4.5000 -4.5000 -4.5000 -4.5000 -4.5000 -4.5000 -4.5000 -4.5000 -4.5000 -4.5000 -4.5000 -4.5000 -4.5000 -4.5000 -4.5000 -4.5000 -4.5000 -4.5000 -4.5000 -4.5000 -4.5000 -4.5000 -4.5000 -4.5000 -4.5000 -4.5000 -4.5000 -4.5000 -4.4000 -4.4000 -4.4000 -4.4000 -4.4000 -4.4000 -4.4000 -4.4000 -4.4000 -4.4000 -4.4000 -4.4000 -4.4000 -4.4000 -4.4000 -4.4000 -4.4000 -4.4000 -4.4000 -4.4000 -4.4000 -4.4000 -4.4000 -4.4000 -4.4000 -4.4000 -4.4000 -4.4000 -4.4000 -4.4000 -4.3000 -4.3000 -4.3000 -4.3000 -4.3000 -4.3000 -4.3000 -4.3000 -4.3000 -4.3000 -4.3000 -4.3000 -4.3000 -4.3000 -4.3000 -4.3000 -4.3000 -4.3000 -4.3000 -4.3000 -4.3000 -4.3000 -4.3000 -4.3000 -4.3000 -4.3000 -4.3000 -4.3000 -4.3000 -4.3000 -4.2000 -4.2000 -4.2000 -4.2000 -4.2000 -4.2000 -4.2000 -4.2000 -4.2000 -4.2000 -4.2000 -4.2000 -4.2000 -4.2000 -4.2000 -4.2000 -4.2000 -4.2000 -4.2000 -4.2000 -4.2000 -4.2000 -4.2000 -4.2000 -4.2000 -4.2000 -4.2000 -4.2000 -4.2000 -4.2000 -4.1000 -4.1000 -4.1000 -4.1000 -4.1000 -4.1000 -4.1000 -4.1000 -4.1000 -4.1000 -4.1000 -4.1000 -4.1000 -4.1000 -4.1000 -4.1000 -4.1000 -4.1000 -4.1000 -4.1000 -4.1000 -4.1000 -4.1000 -4.1000 -4.1000 -4.1000 -4.1000 -4.1000 -4.1000 -4.1000

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u = randn(size(X))                                          % Create 'u'

u = 101x101

1.0416 0.4976 -2.2880 -1.0695 -0.8975 -0.4864 0.4944 -1.0780 0.3432 -1.3906 -0.8808 -0.2443 -0.3013 -2.0142 0.9431 1.3709 0.2739 -0.2580 0.1103 -0.7482 -2.0283 0.2410 -1.1488 -0.6631 1.7010 -1.2080 -0.2023 -0.6565 1.0763 -0.0878 1.3527 1.4142 -0.0716 0.3693 1.1543 -0.5840 -0.5118 0.5214 0.4985 0.9944 -0.5191 -0.1141 -0.1074 0.6984 -0.3814 1.0227 0.1055 -1.2210 0.3175 0.8149 -0.3643 -0.1991 0.3324 -0.8687 -0.1069 1.3456 -0.9496 -2.1156 -0.2499 -0.1036 0.4042 1.1743 0.2479 -2.3132 0.1202 -0.7791 -0.0597 0.5700 2.8162 0.3367 -0.8705 -0.3162 0.7878 0.9872 0.3854 -1.1486 -0.6201 -0.5917 1.2014 1.3259 0.1597 -0.1633 -1.8826 1.6549 0.6298 -0.3307 -1.6883 1.7347 0.4500 -0.1470 -0.7712 0.4800 1.1768 -0.1610 1.9788 0.3717 -2.2490 -0.6272 -0.6400 0.0349 0.8406 -0.9202 -0.9976 0.2482 0.4563 1.1361 0.7043 0.8445 0.6107 1.7341 0.5329 0.3174 0.0295 -0.0537 -0.3833 1.5817 0.8829 -0.9185 -0.3143 -1.1993 0.8611 -2.1310 -0.0497 0.4518 -0.7790 0.9275 -1.5043 -0.1288 -1.7874 -0.5009 -1.2705 1.3980 -1.3038 0.2154 -0.4194 0.9504 -0.8425 -0.8133 1.0367 -0.2383 2.5223 1.0083 1.4929 -0.6683 0.3278 0.9372 -0.5033 -0.9065 -2.1159 0.2459 1.2965 -1.5650 -0.8090 -0.1178 1.5025 0.9455 -0.6591 -0.6878 0.7525 -0.1871 -1.1075 -1.2565 -0.0721 -2.1150 -0.7080 1.7625 -2.0217 -1.0684 0.0492 -0.2358 -0.1170 0.7115 1.0866 -1.9088 0.0407 0.4413 1.4944 -0.4083 -1.8841 -0.7866 0.5496 1.6776 0.4096 -0.4035 0.2178 -0.7906 -0.6781 -1.1891 0.3486 0.1296 -0.6972 -0.6551 -0.5619 1.0872 0.4286 -0.0118 1.6627 -1.0485 0.0341 -0.3043 1.4241 -1.2172 -2.2957 0.2514 -0.4173 -1.5030 1.0038 -0.9420 -1.4555 -1.8242 -0.7110 0.4111 -0.3904 1.8031 0.8705 -0.3651 0.2371 -1.9094 0.7863 -0.2426 1.3816 -0.4364 -1.1832 1.3150 -0.0649 -0.8644 -0.3579 -1.6717 -0.1891 -0.2216 1.0190 -1.6997 0.4288 0.3433 -0.9386 0.0676 -0.8425 -0.4357 0.2615 0.8959 -0.3203 0.7464 0.8151 -1.5330 0.3958 0.1805 -0.2196 0.4440 -1.2297 0.9078 1.0784 1.7844 -0.1735 1.2498 -0.7078 -0.7875 -0.0438 0.1261 0.2567 -0.1855 1.1591 1.3024 -1.2647 0.8526 0.5216 -0.0172 0.1848 0.7712 1.0400 0.6685 0.2125 -1.8611 0.1688 -0.4213 1.1177 -1.4098 -0.4382 -0.4309 -0.3949 -0.4577 0.2615 -0.4372 -0.0506 0.0360 -0.5793 -0.5615 1.1188 1.0681 0.0527 -0.6043 -0.3399 -1.3529 -0.3800 0.6060 -0.7423 -0.8488 0.0508 0.5837 0.2594 0.9917

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

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

ix = sub2ind(size(u), 1:size(u,1), 1:size(u,2));            % Linear Inmdex To Create 'u' With Diagnonal 'Inf'

X(ix) = [];
X = reshape(X,100,[])

X = 100x101

-5.0000 -4.9000 -4.8000 -4.7000 -4.6000 -4.5000 -4.4000 -4.3000 -4.2000 -4.1000 -4.0000 -3.9000 -3.8000 -3.7000 -3.6000 -3.5000 -3.4000 -3.3000 -3.2000 -3.1000 -3.0000 -2.9000 -2.8000 -2.7000 -2.6000 -2.5000 -2.4000 -2.3000 -2.2000 -2.1000 -5.0000 -4.9000 -4.8000 -4.7000 -4.6000 -4.5000 -4.4000 -4.3000 -4.2000 -4.1000 -4.0000 -3.9000 -3.8000 -3.7000 -3.6000 -3.5000 -3.4000 -3.3000 -3.2000 -3.1000 -3.0000 -2.9000 -2.8000 -2.7000 -2.6000 -2.5000 -2.4000 -2.3000 -2.2000 -2.1000 -5.0000 -4.9000 -4.8000 -4.7000 -4.6000 -4.5000 -4.4000 -4.3000 -4.2000 -4.1000 -4.0000 -3.9000 -3.8000 -3.7000 -3.6000 -3.5000 -3.4000 -3.3000 -3.2000 -3.1000 -3.0000 -2.9000 -2.8000 -2.7000 -2.6000 -2.5000 -2.4000 -2.3000 -2.2000 -2.1000 -5.0000 -4.9000 -4.8000 -4.7000 -4.6000 -4.5000 -4.4000 -4.3000 -4.2000 -4.1000 -4.0000 -3.9000 -3.8000 -3.7000 -3.6000 -3.5000 -3.4000 -3.3000 -3.2000 -3.1000 -3.0000 -2.9000 -2.8000 -2.7000 -2.6000 -2.5000 -2.4000 -2.3000 -2.2000 -2.1000 -5.0000 -4.9000 -4.8000 -4.7000 -4.6000 -4.5000 -4.4000 -4.3000 -4.2000 -4.1000 -4.0000 -3.9000 -3.8000 -3.7000 -3.6000 -3.5000 -3.4000 -3.3000 -3.2000 -3.1000 -3.0000 -2.9000 -2.8000 -2.7000 -2.6000 -2.5000 -2.4000 -2.3000 -2.2000 -2.1000 -5.0000 -4.9000 -4.8000 -4.7000 -4.6000 -4.5000 -4.4000 -4.3000 -4.2000 -4.1000 -4.0000 -3.9000 -3.8000 -3.7000 -3.6000 -3.5000 -3.4000 -3.3000 -3.2000 -3.1000 -3.0000 -2.9000 -2.8000 -2.7000 -2.6000 -2.5000 -2.4000 -2.3000 -2.2000 -2.1000 -5.0000 -4.9000 -4.8000 -4.7000 -4.6000 -4.5000 -4.4000 -4.3000 -4.2000 -4.1000 -4.0000 -3.9000 -3.8000 -3.7000 -3.6000 -3.5000 -3.4000 -3.3000 -3.2000 -3.1000 -3.0000 -2.9000 -2.8000 -2.7000 -2.6000 -2.5000 -2.4000 -2.3000 -2.2000 -2.1000 -5.0000 -4.9000 -4.8000 -4.7000 -4.6000 -4.5000 -4.4000 -4.3000 -4.2000 -4.1000 -4.0000 -3.9000 -3.8000 -3.7000 -3.6000 -3.5000 -3.4000 -3.3000 -3.2000 -3.1000 -3.0000 -2.9000 -2.8000 -2.7000 -2.6000 -2.5000 -2.4000 -2.3000 -2.2000 -2.1000 -5.0000 -4.9000 -4.8000 -4.7000 -4.6000 -4.5000 -4.4000 -4.3000 -4.2000 -4.1000 -4.0000 -3.9000 -3.8000 -3.7000 -3.6000 -3.5000 -3.4000 -3.3000 -3.2000 -3.1000 -3.0000 -2.9000 -2.8000 -2.7000 -2.6000 -2.5000 -2.4000 -2.3000 -2.2000 -2.1000 -5.0000 -4.9000 -4.8000 -4.7000 -4.6000 -4.5000 -4.4000 -4.3000 -4.2000 -4.1000 -4.0000 -3.9000 -3.8000 -3.7000 -3.6000 -3.5000 -3.4000 -3.3000 -3.2000 -3.1000 -3.0000 -2.9000 -2.8000 -2.7000 -2.6000 -2.5000 -2.4000 -2.3000 -2.2000 -2.1000

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

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

Y(ix) = [];
Y = reshape(Y,100,[])

Y = 100x101

-4.9000 -5.0000 -5.0000 -5.0000 -5.0000 -5.0000 -5.0000 -5.0000 -5.0000 -5.0000 -5.0000 -5.0000 -5.0000 -5.0000 -5.0000 -5.0000 -5.0000 -5.0000 -5.0000 -5.0000 -5.0000 -5.0000 -5.0000 -5.0000 -5.0000 -5.0000 -5.0000 -5.0000 -5.0000 -5.0000 -4.8000 -4.8000 -4.9000 -4.9000 -4.9000 -4.9000 -4.9000 -4.9000 -4.9000 -4.9000 -4.9000 -4.9000 -4.9000 -4.9000 -4.9000 -4.9000 -4.9000 -4.9000 -4.9000 -4.9000 -4.9000 -4.9000 -4.9000 -4.9000 -4.9000 -4.9000 -4.9000 -4.9000 -4.9000 -4.9000 -4.7000 -4.7000 -4.7000 -4.8000 -4.8000 -4.8000 -4.8000 -4.8000 -4.8000 -4.8000 -4.8000 -4.8000 -4.8000 -4.8000 -4.8000 -4.8000 -4.8000 -4.8000 -4.8000 -4.8000 -4.8000 -4.8000 -4.8000 -4.8000 -4.8000 -4.8000 -4.8000 -4.8000 -4.8000 -4.8000 -4.6000 -4.6000 -4.6000 -4.6000 -4.7000 -4.7000 -4.7000 -4.7000 -4.7000 -4.7000 -4.7000 -4.7000 -4.7000 -4.7000 -4.7000 -4.7000 -4.7000 -4.7000 -4.7000 -4.7000 -4.7000 -4.7000 -4.7000 -4.7000 -4.7000 -4.7000 -4.7000 -4.7000 -4.7000 -4.7000 -4.5000 -4.5000 -4.5000 -4.5000 -4.5000 -4.6000 -4.6000 -4.6000 -4.6000 -4.6000 -4.6000 -4.6000 -4.6000 -4.6000 -4.6000 -4.6000 -4.6000 -4.6000 -4.6000 -4.6000 -4.6000 -4.6000 -4.6000 -4.6000 -4.6000 -4.6000 -4.6000 -4.6000 -4.6000 -4.6000 -4.4000 -4.4000 -4.4000 -4.4000 -4.4000 -4.4000 -4.5000 -4.5000 -4.5000 -4.5000 -4.5000 -4.5000 -4.5000 -4.5000 -4.5000 -4.5000 -4.5000 -4.5000 -4.5000 -4.5000 -4.5000 -4.5000 -4.5000 -4.5000 -4.5000 -4.5000 -4.5000 -4.5000 -4.5000 -4.5000 -4.3000 -4.3000 -4.3000 -4.3000 -4.3000 -4.3000 -4.3000 -4.4000 -4.4000 -4.4000 -4.4000 -4.4000 -4.4000 -4.4000 -4.4000 -4.4000 -4.4000 -4.4000 -4.4000 -4.4000 -4.4000 -4.4000 -4.4000 -4.4000 -4.4000 -4.4000 -4.4000 -4.4000 -4.4000 -4.4000 -4.2000 -4.2000 -4.2000 -4.2000 -4.2000 -4.2000 -4.2000 -4.2000 -4.3000 -4.3000 -4.3000 -4.3000 -4.3000 -4.3000 -4.3000 -4.3000 -4.3000 -4.3000 -4.3000 -4.3000 -4.3000 -4.3000 -4.3000 -4.3000 -4.3000 -4.3000 -4.3000 -4.3000 -4.3000 -4.3000 -4.1000 -4.1000 -4.1000 -4.1000 -4.1000 -4.1000 -4.1000 -4.1000 -4.1000 -4.2000 -4.2000 -4.2000 -4.2000 -4.2000 -4.2000 -4.2000 -4.2000 -4.2000 -4.2000 -4.2000 -4.2000 -4.2000 -4.2000 -4.2000 -4.2000 -4.2000 -4.2000 -4.2000 -4.2000 -4.2000 -4.0000 -4.0000 -4.0000 -4.0000 -4.0000 -4.0000 -4.0000 -4.0000 -4.0000 -4.0000 -4.1000 -4.1000 -4.1000 -4.1000 -4.1000 -4.1000 -4.1000 -4.1000 -4.1000 -4.1000 -4.1000 -4.1000 -4.1000 -4.1000 -4.1000 -4.1000 -4.1000 -4.1000 -4.1000 -4.1000

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

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

u(ix) = [];
u = reshape(u, 100,[])

u = 100x101

1.3527 0.4976 -2.2880 -1.0695 -0.8975 -0.4864 0.4944 -1.0780 0.3432 -1.3906 -0.8808 -0.2443 -0.3013 -2.0142 0.9431 1.3709 0.2739 -0.2580 0.1103 -0.7482 -2.0283 0.2410 -1.1488 -0.6631 1.7010 -1.2080 -0.2023 -0.6565 1.0763 -0.0878 0.4042 1.1743 -0.0716 0.3693 1.1543 -0.5840 -0.5118 0.5214 0.4985 0.9944 -0.5191 -0.1141 -0.1074 0.6984 -0.3814 1.0227 0.1055 -1.2210 0.3175 0.8149 -0.3643 -0.1991 0.3324 -0.8687 -0.1069 1.3456 -0.9496 -2.1156 -0.2499 -0.1036 -0.7712 0.4800 1.1768 -2.3132 0.1202 -0.7791 -0.0597 0.5700 2.8162 0.3367 -0.8705 -0.3162 0.7878 0.9872 0.3854 -1.1486 -0.6201 -0.5917 1.2014 1.3259 0.1597 -0.1633 -1.8826 1.6549 0.6298 -0.3307 -1.6883 1.7347 0.4500 -0.1470 0.8611 -2.1310 -0.0497 0.4518 1.9788 0.3717 -2.2490 -0.6272 -0.6400 0.0349 0.8406 -0.9202 -0.9976 0.2482 0.4563 1.1361 0.7043 0.8445 0.6107 1.7341 0.5329 0.3174 0.0295 -0.0537 -0.3833 1.5817 0.8829 -0.9185 -0.3143 -1.1993 1.2965 -1.5650 -0.8090 -0.1178 1.5025 0.9275 -1.5043 -0.1288 -1.7874 -0.5009 -1.2705 1.3980 -1.3038 0.2154 -0.4194 0.9504 -0.8425 -0.8133 1.0367 -0.2383 2.5223 1.0083 1.4929 -0.6683 0.3278 0.9372 -0.5033 -0.9065 -2.1159 0.2459 0.5496 1.6776 0.4096 -0.4035 0.2178 -0.7906 -0.6591 -0.6878 0.7525 -0.1871 -1.1075 -1.2565 -0.0721 -2.1150 -0.7080 1.7625 -2.0217 -1.0684 0.0492 -0.2358 -0.1170 0.7115 1.0866 -1.9088 0.0407 0.4413 1.4944 -0.4083 -1.8841 -0.7866 -0.7110 0.4111 -0.3904 1.8031 0.8705 -0.3651 0.2371 -1.1891 0.3486 0.1296 -0.6972 -0.6551 -0.5619 1.0872 0.4286 -0.0118 1.6627 -1.0485 0.0341 -0.3043 1.4241 -1.2172 -2.2957 0.2514 -0.4173 -1.5030 1.0038 -0.9420 -1.4555 -1.8242 -0.3203 0.7464 0.8151 -1.5330 0.3958 0.1805 -0.2196 0.4440 0.7863 -0.2426 1.3816 -0.4364 -1.1832 1.3150 -0.0649 -0.8644 -0.3579 -1.6717 -0.1891 -0.2216 1.0190 -1.6997 0.4288 0.3433 -0.9386 0.0676 -0.8425 -0.4357 0.2615 0.8959 0.2125 -1.8611 0.1688 -0.4213 1.1177 -1.4098 -0.4382 -0.4309 -0.3949 0.9078 1.0784 1.7844 -0.1735 1.2498 -0.7078 -0.7875 -0.0438 0.1261 0.2567 -0.1855 1.1591 1.3024 -1.2647 0.8526 0.5216 -0.0172 0.1848 0.7712 1.0400 0.6685 1.5556 0.3735 0.4183 -2.1705 0.7528 -0.4665 -0.0809 -1.3804 -0.6506 -1.0935 0.2615 -0.4372 -0.0506 0.0360 -0.5793 -0.5615 1.1188 1.0681 0.0527 -0.6043 -0.3399 -1.3529 -0.3800 0.6060 -0.7423 -0.8488 0.0508 0.5837 0.2594 0.9917

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

Then do the same sort of operation with ‘v’.

Another (perhaps preferable) option is to leave them as NaN values. The NaN values will not plot, and any calculations involving them will also be NaN, however if there are any recursive operations involving the matrices, that could leave many more elements a NaN values.

As I mentioned earlier, fillmissing has other options to fill the NaN values if you want to use them, for example a constant value. Interpolating them using linear or other methods is not absolutely necessary.

.

[feynman feynman]

Thank you so much!

[Star Strider]

As always, my pleasure!