Interpolation of a table (N dependent over M independent) variables

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Andrey Mironenko
Andrey Mironenko 2020-2-11
编辑： Andrey Mironenko 2020-2-11
I have the given table:
% AoA vel CDa CLa CSa Cmxa Cmya Cmza
-40 5 1.14668 -1.13299 0.007386 -0.00189 0.00032 -0.06785
-30 5 0.792945 -1.05145 0.001173 0.000308 -0.00066 -0.05284
-20 5 0.46091 -0.9383 0.004354 -0.00228 -0.00047 -0.03891
-10 5 0.176381 -0.70155 -0.0037 0.000787 2.33E-07 -0.02382
10 5 0.189115 1.388965 -0.00634 0.002562 -0.00117 0.024032
15 5 0.309158 1.551853 -0.00271 5.05E-05 0.000825 0.033537
20 5 0.640063 1.489591 -0.02716 0.006253 -0.00117 0.038003
30 5 0.843689 1.382815 0.004803 -0.00204 -0.00216 0.050888
45 5 1.435123 1.337931 -0.01323 0.001252 -0.00139 0.069426
-40 10 1.135769 -1.12232 0.008136 -0.00338 0.000516 -0.13411
-30 10 0.786587 -1.04173 0.000678 0.001555 -0.00207 -0.10408
-20 10 0.454954 -0.93194 0.002149 -0.00573 -0.00102 -0.07623
-10 10 0.17226 -0.70207 -0.0055 0.001301 0.000137 -0.04808
10 10 0.185599 1.40734 -0.0057 0.001352 -0.00197 0.047792
15 10 0.302297 1.554765 -0.00804 0.000129 -0.00238 0.068301
20 10 0.635433 0.917047 -0.02498 0.007075 -0.00257 0.089213
30 10 0.840859 1.379861 0.008731 -0.00259 -0.00295 0.101972
45 10 1.600783 1.628605 -0.00913 0.000973 -0.0029 0.073734
-40 15 1.134235 -1.12156 0.008302 -0.00298 0.00131 -0.13368
-30 15 0.78341 -1.0394 0.0019 0.001564 -0.00192 -0.10364
-20 15 0.456106 -0.93554 0.003156 -0.00523 -0.00066 -0.07602
-10 15 0.17026 -0.69772 -0.0069 0.00247 0.000491 -0.04811
10 15 0.183753 1.411952 -0.00514 0.001282 -0.0019 0.047676
15 15 0.306336 1.572716 -0.02108 -0.00823 -0.00079 0.068636
20 15 0.446206 1.49619 -0.02127 0.004692 -0.00195 0.075722
30 15 0.794182 1.287936 0.024425 -0.00247 -0.00354 0.119258
45 15 1.564058 1.411367 -0.01153 0.00088 -0.0027 0.158261
-40 20 1.130857 -1.11942 0.011118 -0.00251 0.001287 -0.13325
-30 20 0.783355 -1.03969 0.001792 0.001849 -0.00195 -0.10357
-20 20 0.453382 -0.93397 0.004538 -0.00527 -0.00116 -0.07611
-10 20 0.169203 -0.70222 -0.00304 0.001263 -0.00043 -0.04776
10 20 0.182575 1.414132 -0.00465 0.001476 -0.00181 0.047619
15 20 0.302945 1.557986 -0.02611 -0.01088 -0.00131 0.069002
20 20 0.441594 1.667185 -0.03727 0.018395 -0.001 0.085187
30 20 0.833129 1.367756 0.021856 -0.00202 -0.00372 0.100927
45 20 1.414648 1.322671 -0.01084 0.000713 -0.00222 0.137053
-40 25 1.130254 -1.11854 0.011035 -0.0026 0.001291 -0.13307
-30 25 0.78033 -1.03801 0.006084 0.001668 -0.00065 -0.10342
-20 25 0.454824 -0.93126 -3.40E-06 -0.00521 -0.00019 -0.07559
-10 25 0.167459 -0.69818 -0.0036 0.002762 0.000251 -0.04732
10 25 0.181822 1.416205 -0.00467 0.001572 -0.00181 0.047616
15 25 0.307287 1.595218 -0.01342 -0.00625 -0.00398 0.072986
20 25 0.449001 1.491301 -0.03155 0.01809 -0.00051 0.074918
30 25 0.853412 1.258704 0.017987 -0.00277 -0.00635 0.089384
45 25 1.543683 1.460518 -0.01509 -0.00111 -0.00411 0.14807
-40 30 1.125591 -1.11304 0.00975 -0.0033 0.001251 -0.13249
-30 30 0.780225 -1.0392 0.007942 0.002208 -0.00092 -0.10343
-20 30 0.453074 -0.9373 0.002856 -0.00568 -0.00058 -0.07605
-10 30 0.166888 -0.7011 -0.00378 0.002011 6.89E-05 -0.04708
10 30 0.181113 1.41787 -0.00429 0.001623 -0.00172 0.04744
15 30 0.309359 1.560654 -0.01769 -0.00408 -0.00315 0.073177
20 30 0.424315 1.507518 -0.0368 0.019651 -0.0007 0.076681
30 30 0.829189 1.368019 0.008733 -0.00308 -0.00554 0.100652
45 30 1.416077 1.324257 -0.01235 -0.00132 -0.00327 0.137079
-40 35 1.128033 -1.11649 0.009527 -0.00154 0.000553 -0.06634
-30 35 0.781222 -1.04085 0.003488 0.001143 -0.00094 -0.05175
-20 35 0.452797 -0.93978 0.003031 -0.0029 -0.0004 -0.03791
-10 35 0.16714 -0.70461 6.22E-05 0.000711 -0.00057 -0.02364
10 35 0.180499 1.418791 -0.00459 0.000791 -0.0009 0.02378
15 35 0.303655 1.545379 -0.01416 -0.00088 -0.0001 0.035761
20 35 0.477813 1.317009 -0.03607 0.011042 0.000731 0.036779
30 35 0.833212 1.306725 -0.01089 -0.00047 -0.00033 0.048257
45 35 1.387943 1.286388 -0.01112 -0.00306 -0.00334 0.060734
-40 40 1.130903 -1.12077 0.008338 -0.00266 0.001092 -0.13321
-30 40 0.779686 -1.03895 0.007888 0.002269 -0.00089 -0.10334
-20 40 0.450783 -0.93033 0.000821 -0.00456 0.00064 -0.07527
-10 40 0.166252 -0.70388 -0.00162 0.001372 -0.00043 -0.04719
10 40 0.180017 1.420129 -0.00468 0.001581 -3.60E-05 0.047408
15 40 0.302535 1.530176 -0.00861 0.005485 -0.00278 0.071903
20 40 0.447166 1.489379 -0.03367 0.020146 0.003072 0.074659
30 40 0.831837 1.375903 -0.01245 7.12E-05 -0.00282 0.10027
45 40 1.41966 1.329288 -0.00866 -0.00745 -0.00655 0.137312
Independent variables: ,
Dependent variables: , , , , ,
So far I have the solution for the any of dependent variables, e.g. for :
AoA = [-40, -30, -20, -10, 10, 15, 20, 30, 45]; % 9 elements
vel = [5, 10, 15, 20, 25, 30, 35, 40]; % 8 elements
CDa = [1.14668, 0.792945, 0.46091, 0.176381, 0.189115, 0.309158, 0.640063, 0.843689, 1.435123,
1.135769, 0.786587, 0.454954, 0.17226, 0.185599, 0.302297, 0.635433, 0.840859, 1.600783,
1.134235, 0.78341, 0.456106, 0.17026, 0.183753, 0.306336, 0.446206, 0.794182, 1.564058,
1.130857, 0.783355, 0.453382, 0.169203, 0.182575, 0.302945, 0.441594, 0.833129, 1.414648,
1.130254, 0.78033, 0.454824, 0.167459, 0.181822, 0.307287, 0.449001, 0.853412, 1.543683,
1.125591, 0.780225, 0.453074, 0.166888, 0.181113, 0.309359, 0.424315, 0.829189, 1.416077,
1.128033, 0.781222, 0.452797, 0.16714, 0.180499, 0.303655, 0.477813, 0.833212, 1.387943,
1.130903, 0.779686, 0.450783, 0.166252, 0.180017, 0.302535, 0.447166, 0.831837, 1.41966]; % 72 elements
CDa_i = griddata (AoA, vel, CDa, -35, 7); % interpolated however only one column, namely CDa
So I need to transform every dependent variable (column) into 1D-array size of (NxM) and use griddata
This could be done for all columns but the process is tedious and error-prone.
I need somewhat like: [, , , , , ] = interpNxM(, );
The question: Does a simpler solution exist?

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