remove inf in quiver

Question

feynman feynman 2024-4-28

0
链接

此问题的直接链接

https://ww2.mathworks.cn/matlabcentral/answers/2112596-remove-inf-in-quiver

评论： Star Strider 2024-4-29

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);

1 个评论
显示 -1更早的评论隐藏 -1更早的评论

Dyuman Joshi 2024-4-28

u and v aren't defined in the code.

请先登录，再进行评论。

请先登录，再回答此问题。

Answer 1

Star Strider 2024-4-28

1
链接

此回答的直接链接

https://ww2.mathworks.cn/matlabcentral/answers/2112596-remove-inf-in-quiver#answer_1449081

在 MATLAB Online 中打开

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

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

.

4 个评论
显示 2更早的评论隐藏 2更早的评论

Star Strider 2024-4-28

在 MATLAB Online 中打开

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
<mw-icon class=""></mw-icon>
<mw-icon class=""></mw-icon>
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
<mw-icon class=""></mw-icon>
<mw-icon class=""></mw-icon>
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
<mw-icon class=""></mw-icon>
<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 2024-4-29