我正在研究我的论文，我试图读出已经数字化的经验图。当然，这可以通过数据光标来完成。但是我需要自动化这个过程，因为图中的数据将以迭代的方式用于其他一些方程。
此图表包含不同的数据集。 X轴具有恒定范围，而Y值取决于翼型的厚度。因此，与具有35％厚度的翼型相比，30％厚度具有不同的数据集。
现在我知道如何用书面代码阅读图表。但是，当我计算出的翼型厚度为30％或％35时，我才知道它，因为我有数值数据集。
有时，我计算出一个30％到35％之间的翼型厚度，对于这种情况，我没有具体的数据集，请参阅下面链接中红色和蓝色线之间的空格：
蓝线对应30％厚度数据集，红线对应35％厚度数据集。
http://i.imgur.com/YUs6RYO.jpg
那么如何插入这个三维问题呢？我真的不需要额外的数据集，比如说厚度为32％，只需要Y值即可。
（例如，假设我计算了32％的厚度，我需要对应于X = 4的Y值）。还计算X，使其为每次迭代的常数。此外，不一定要保存插值。
以下是30％的数据集：
-0.003287690575036084 0.8053924336902605
0.043171086183339424 0.8050602124925952
0.08963141038123656 0.8049872858882297
0.1360917345791337 0.804914359283864
0.18255205877703085 0.8048414326794985
0.22901393041444962 0.8050278006684327
0.2754727071728251 0.8046955794707673
0.32193303137072227 0.8046226528664018
0.3683933555686194 0.8045497262620362
0.41485367976651655 0.8044767996576706
0.4613140039644137 0.8044038730533051
0.5077928974365709 0.807442481568537
0.554253221634468 0.8073695549641714
0.6007135458323651 0.8072966283598059
0.6471738700302623 0.8072237017554403
0.6936341942281594 0.8071507751510747
0.7400945184260566 0.8070778485467092
0.7865548426239537 0.8070049219423436
0.8330151668218508 0.806931995337978
0.879475491019748 0.8068590687336125
0.9259358152176451 0.8067861421292469
0.9723914970969771 0.805935331744982
1.0188580110529613 0.8068995835138155
1.0653616635574645 0.8140869055218445
1.1118746006990976 0.8228299950896718
1.1556231476667052 0.8317626359505925
1.2029940294916959 0.8452458101065382
1.2514872249891795 0.8614276158935545
1.2980594290644916 0.8801016883847644
1.3428742623455985 0.8991957518202068
1.3834632352399387 0.9179287180691845
1.4240523769458633 0.9366899710010677
1.4670993309116496 0.9544431793698794
1.5108094611858853 0.9771597061348987
1.5560453105579366 0.996026813137967
1.599090576407881 1.0134971546777245
1.6396876522582617 1.033587881706163
1.6802729112977504 1.0516985409312212
1.721977391140349 1.0686193915565696
1.759264865239414 1.0885371057408935
1.8027351648960062 1.106453713901426
1.8475635031038473 1.127810711969303
1.8899200176903397 1.1478279599191006
1.9329627513665215 1.1648740012152765
1.9743393991126437 1.185840399970377
2.007525709721886 1.1997514536139606
2.04520726180335 1.2178772782578813
2.0882668766379497 1.2377519878446006
2.12885563007723 1.2564481814058013
2.169441108571778 1.2745956133186365
2.214272434744659 1.2964532856739395
2.254879183498814 1.3181648396328591
2.2979393047681658 1.3381244092682947
2.3385460028788447 1.3598274772223429
2.3773808546019755 1.3795249530614826
2.416215706325106 1.3992224289006225
2.4568104188133093 1.418917142368385
2.501617655573127 1.4367383050730829
2.548189395416583 1.4553345891863025
2.593000852466006 1.4738629189636363
2.641319257635704 1.490245364991704
2.6840014566601855 1.5058571953458966
2.7334538841264777 1.5205811105296216
2.780967627778736 1.534832690900812
2.8274836597994124 1.5440943696552392
2.875656557422194 1.5529459809557538
2.9205079866431563 1.5613212541975945
2.9670069968290953 1.567730692425724
3.013495174938382 1.5723250685007546
3.0599771632895827 1.5758822662025862
3.106451414443174 1.5781429909379188
3.1529287604758096 1.580922304859851
3.1994030116294008 1.5831830295951836
3.2458849999806016 1.5867402272970152
3.2923499664970635 1.587445184472549
3.338818027892569 1.5886687308346825
3.38527216233238 1.5875586258571177
3.4317108223769734 1.583855574946555
3.4781726940143933 1.5840419429354893
3.5246314707727677 1.5837097217378238
3.5710763205754494 1.5810438492004604
3.6175258126966945 1.579155860442996
3.6639675676203325 1.5759713987190331
3.7131639859552887 1.5709675339726639
3.7568433402699988 1.5683060023046078
3.8032742631169842 1.563306478427546
3.8497082808430134 1.558825543737084
3.896136108810956 1.5533074306734227
3.94256548421842 1.5480486122030612
3.988994859625884 1.5427897937326998
4.035421140154305 1.5370123860757385
4.081836588606075 1.529419916265679
4.128252037057845 1.5218274464556192
4.174667485509614 1.5142349766455596
4.221086028840427 1.5071610960220996
4.264731118422872 1.4987580412166908
4.31392156806253 1.4927540401818795
4.360340111393343 1.4856801595584195
4.406757107284634 1.4783469843416595
4.453175650615448 1.4712731037181994
4.499600383704346 1.4652364014679387
4.546023569353724 1.458940404624378
4.592451397321666 1.4534222915607167
4.638882320168652 1.4484227676836547
4.685311695576116 1.4431639492132935
4.73173952354406 1.437645836149632
4.778173541270088 1.43316490145917
4.824602916677553 1.4279060829888084
4.871050861359277 1.4257587996380445
4.917486426524827 1.4215371595408821
4.963921991690379 1.41731551944372
5.010357556855929 1.4130938793465577
5.056800859219089 1.4101687122158943
5.103258088537942 1.409577196424929
5.149710675538231 1.4082077968540645
5.188422196331486 1.407239493607210
和35％案例
0.006168966650546004 0.8044498654339525
0.05251074466351513 0.8041649550926027
0.09885097179873537 0.8041405922130573
0.1451911989339556 0.8041162293335119
0.19153142606917584 0.8040918664539666
0.2378701023266474 0.8043280510362257
0.28421188033961653 0.8040431406948759
0.33055210747483676 0.8040187778153305
0.376892334610057 0.8039944149357852
0.42323256174527746 0.8039700520562398
0.4695727888804977 0.8039456891766945
0.5158944054827321 0.8070478958388025
0.5622346326179524 0.8070235329592572
0.6085748597531726 0.8069991700797119
0.6549150868883928 0.8069748072001666
0.7012553140236131 0.8069504443206211
0.7475955411588333 0.8069260814410758
0.7939357682940535 0.8069017185615305
0.8402759954292742 0.8068773556819852
0.8866162225644945 0.8068529928024398
0.9329564496997147 0.8068286299228944
0.9793013294681812 0.8060226246579357
1.0256369039701552 0.8067799041638037
1.071932155650659 0.8143114176765875
1.1187799629176767 0.8216937568912026
1.1617476956110715 0.8321674748214614
1.20883433805141 0.8457655870362711
1.2570081679490785 0.8620765348918837
1.3032362666230588 0.8808897535008002
1.347706558159841 0.9001231394982395
1.3879672317285157 0.9188329081527227
1.4282258750572274 0.9378837571208412
1.472706881749366 0.9553169970185403
1.5124760398627677 0.9779636470492524
1.551459748890828 0.9949497419759024
1.5941903785600746 1.0115548900587519
1.6344423390155782 1.0317284617259186
1.6747003901909685 1.0508787924521803
1.71742594426031 1.0683366413191173
1.7594253491698706 1.0898309763652727
1.8021487038125867 1.1076583289053143
1.8423992261814504 1.1280734991279724
1.8826578695101626 1.1471243480960909
1.9271326726913052 1.1655997778410079
1.972291271970799 1.1871540357272572
2.00704079571732 1.202828964772921
2.0444053954250387 1.2215082798279724
2.0871182604946177 1.2410978806551278
2.127378223479305 1.2599270274193834
2.167638930885312 1.2786311114019728
2.2120949825445826 1.3002567968214347
2.2565756226656166 1.3177516206646513
2.2968395953742293 1.335907133809478
2.3370983232962734 1.3549437710978616
2.381577849605106 1.3726257153909196
2.427805638103535 1.391491043492197
2.4778943367576924 1.4104846150844144
2.5171966806686945 1.42505456449785
2.5645930555508722 1.44222572502541
2.6111147353827078 1.458057391232824
2.6590291034329954 1.4716178473302413
2.7036702332173843 1.4790013466819776
2.751618055916255 1.4869414218176131
2.801789183722762 1.492086895814924
2.8414799006551674 1.4970182761743256
2.890545684656986 1.5025011808872442
2.936856445114978 1.5074272197819834
2.9831823266310216 1.5098129209241293
3.0294888221752045 1.5154554653388308
3.079683755955156 1.5166015357974434
3.1221351571351703 1.521138783739438
3.168472282514893 1.5216355157835015
3.2147985517503748 1.5239560800601961
3.261134126252348 1.5247133595660642
3.3074743533875686 1.524688996686519
3.353805275256295 1.5262279185778003
3.4001361971250232 1.5277668404690816
3.4464841786489866 1.5264397402805139
3.492839914561696 1.5238099027829242
3.539192548718906 1.5217011602089432
3.585549835509364 1.5188107752495488
3.631899367911078 1.5172231275991768
3.6810223128356383 1.5131029972913028
3.724610839736495 1.5119634526039971
3.770966575649203 1.5093336151064072
3.8173285150729073 1.5056615877615995
3.8636904544966115 1.5019895604167919
3.909506484952725 1.4973500753909907
3.9580866226651925 1.4917413924785146
4.002793332422961 1.4881074563025198
4.049172331501902 1.4815694068778633
4.0955466779475955 1.4758129998386198
4.141921024393291 1.4700565927993767
4.188298472594484 1.4637790908365245
4.234679022551173 1.4569804939500635
4.28106267426336 1.4496608021399933
4.327446325975549 1.4423411103299233
4.373831528565486 1.434760871058049
4.420213629399925 1.4277017267097833
4.4665972811121115 1.4203820348997134
4.512984034579798 1.4125412481660344
4.559364584536487 1.4057426512795734
4.606304779806543 1.3976038059838376
4.652133438838613 1.390842720197629
4.69851553967305 1.3837835758493635
4.7448991913852385 1.3764638840392935
4.794049165893421 1.3678027836828277
4.826065929248319 1.3644360657570844
第一列是X，第二列是Y
我希望我足够清楚。如果您需要更多信息，请告诉我
非常感谢！
我知道两个数据集都应该用interp1进行插值，以获得一个大小相同且X轴范围相同的数据集。
答案 0 :(得分：0)
这样的事情应该有效：
% Load the data however you want.
data_30 = load('data_30.txt');
data_35 = load('data_35.txt');
x30 = data_30(:,1);
y30 = data_30(:,2);
x35 = data_35(:,1);
y35 = data_35(:,2);
% thicknesses for each dataset
t30 = 30;
t35 = 35;
% Values that you want to interpolate to
xi = 4;
ti = 32;
% Interpolate in x first
y30i = interp1(x30, y30, xi)
y35i = interp1(x35, y35, xi)
% ... then interpolate in t
yi = interp1([t30 t35], [y30i y35i], ti)
% ... or interpolate everything simultaneously
yi = interp1([t30 t35], [interp1(x30, y30, xi) interp1(x35, y35, xi)], ti)