You are now following this question
- You will see updates in your followed content feed.
- You may receive emails, depending on your communication preferences.
remove inf in quiver
2 views (last 30 days)
Show older comments
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 Comment
Dyuman Joshi
on 28 Apr 2024
u and v aren't defined in the code.
Accepted Answer
Star Strider
on 28 Apr 2024
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 Comments
feynman feynman
on 28 Apr 2024
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
on 28 Apr 2024
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
on 29 Apr 2024
Thank you so much!
Star Strider
on 29 Apr 2024
As always, my pleasure!
More Answers (0)
See Also
Categories
Find more on Vector Fields in Help Center and File Exchange
Tags
Community Treasure Hunt
Find the treasures in MATLAB Central and discover how the community can help you!
Start Hunting!An Error Occurred
Unable to complete the action because of changes made to the page. Reload the page to see its updated state.
Select a Web Site
Choose a web site to get translated content where available and see local events and offers. Based on your location, we recommend that you select: .
You can also select a web site from the following list
How to Get Best Site Performance
Select the China site (in Chinese or English) for best site performance. Other MathWorks country sites are not optimized for visits from your location.
Americas
- América Latina (Español)
- Canada (English)
- United States (English)
Europe
- Belgium (English)
- Denmark (English)
- Deutschland (Deutsch)
- España (Español)
- Finland (English)
- France (Français)
- Ireland (English)
- Italia (Italiano)
- Luxembourg (English)
- Netherlands (English)
- Norway (English)
- Österreich (Deutsch)
- Portugal (English)
- Sweden (English)
- Switzerland
- United Kingdom (English)
Asia Pacific
- Australia (English)
- India (English)
- New Zealand (English)
- 中国
- 日本Japanese (日本語)
- 한국Korean (한국어)