How to replace the values of a vector from desired location to its end?
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I have three vectors a, b and c given below. I want to replace all the values of 'a' from my desired location say for example from location 60 to its end such that those new values are asympotically decreasing from its previous values (i.e., location 1 to 59) and are also less in values from the values of vector b and c from the same location i.e., 60 to the last. One method is manual but its very time consuming. I want that MATLAB should do it for me.
a=[0.397514402866404 0.349231851271051 0.425836326161108 0.418823411789294 0.386696621928579 0.384575525674989 0.364845287492425 0.495928922548383 0.479694820530569 0.389582032512016 0.384505211980922 0.333724865928879 0.365919184423201 0.400772506172964 0.379242722219023 0.364459695600443 0.366128522849086 0.385174773969786 0.372867063392224 0.376548774132535 0.473071808102272 0.251627836852150 0.385042011392780 0.381148165802822 0.369065536223563 0.431444009941580 0.414879010564463 0.325227505916048 0.437887598814955 0.428837027000730 0.406640068369860 0.409409452088121 0.424434292672168 0.409932466026339 0.412835839983982 0.399245885507425 0.387575414369059 0.407050454183416 0.326211795299434 0.458434548036028 0.338455259025878 0.368562202826825 0.413767382594240 0.458452216705265 0.432455960242579 0.415225789491909 0.389509369547427 0.408404223427086 0.320887810417104 0.419718890158122 0.411956096059551 0.467265991314386 0.425563572738537 0.457286988716662 0.364096160999733 0.417395179173226 0.415151032279429 0.439401358954262 0.416025807219374 0.349557755864473 0.413126979577490 0.382242156796521 0.387869720649921 0.344424085502606 0.418357738224712 0.393375703492509 0.410207735948398 0.432538723781336 0.460651539221876 0.366947323455358 0.343158385463345 0.360779192277814 0.407775542134092 0.401720168768703 0.411858470659967 0.390296201351616 0.386700151754722 0.393559416101593 0.405571260599836 0.426038375438873 0.351052078565856 0.417357035534662 0.441552934076875 0.400293564991402 0.396957107193780 0.357192556998893 0.431047692995460 0.383175812178153 0.446884989308566 0.441112998092938 0.421424171377919 0.426723330306019 0.311409378918779 0.383676813236671 0.312569975506430 0.429482861741989 0.411346818807958 0.351598372195988 0.387493340782735 0.464061121524104];
b=[0.551682651670431 0.645096882777877 0.581684008069165 0.506704132814463 0.576791071314667 0.598095975405639 0.556468410315606 0.603215166364727 0.573881723497337 0.702829523545135 0.655401596692394 0.637834736633294 0.569663359602261 0.634042823056570 0.520415579093006 0.557141591176942 0.583010935305893 0.520542027714726 0.593618261801875 0.491359612337422 0.541672987084530 0.623432585679211 0.486386829534579 0.604580199387316 0.673723294633068 0.547784751842482 0.633068049834673 0.592470280261216 0.635036043068146 0.584179124192050 0.566186813602900 0.590306702135361 0.592142837346948 0.645760363411235 0.529148516876326 0.542359452487705 0.576111765619481 0.440572920274603 0.673125819553129 0.552989311869312 0.810202391087325 0.616215986476514 0.637502718705395 0.510482159785954 0.636855535171597 0.640515935733207 0.614862120663137 0.602807491865895 0.576035793127878 0.591165927487269 0.574446655730001 0.569647577308512 0.770477592901505 0.555931615848446 0.559585144340360 0.580838963787409 0.675838792335884 0.539290120744169 0.630342008576237 0.614578423728421 0.514912997133050 0.570801392261179 0.557904302582161 0.516333537322627 0.564906015777891 0.578745862292832 0.633282156008040 0.566117556674110 0.620214334996768 0.603670500271155 0.574218976104897 0.567667037444456 0.509795475612059 0.523914908058583 0.636086852547285 0.552641580772836 0.522137540787447 0.499410785060553 0.537599355871173 0.740725123231447 0.646255524703763 0.616982399812298 0.691986273761024 0.661806826749363 0.586985183103399 0.552227356972073 0.576677035029797 0.535296631878600 0.560018190112691 0.616588660122031 0.674001758525734 0.539967859639179 0.515501886822976 0.552872905239819 0.575552960829275 0.528735725333748 0.673536000041314 0.616306055966220 0.596732320262772 0.620430313847279];
c=[1.11459952996087 1.68670358870423 1.22532598107866 0.624815936271779 0.554255279167175 1.15749032120038 0.572704247243950 0.818472371403532 0.610108820878572 0.520393200082739 0.877109078448262 0.584376156817110 0.864813871234754 0.737829446874604 0.580997699895747 1.77068479618745 0.539182799387493 1.62437864878862 0.521003914004704 0.849895522917450 0.577263897904287 0.526984303503854 1.64990069405968 1.53080783202811 0.896341460688281 0.691820506571966 0.839658467823829 0.916296633939690 1.57481829415576 1.11756769475573 0.496358173351652 0.758366460593150 0.603591262006390 0.856744750071039 0.634025989346108 0.862693031299083 0.499801270339618 1.31880150505211 0.497983284511088 0.955339233571990 0.607304623078370 0.720711362625385 0.662375530565266 0.794992908912330 1.27508484531957 0.681838541650462 1.39432785905121 0.921767072964069 0.732218886357660 1.62092196497150 1.05716708049553 0.921938203224915 0.972931972055981 0.785631875908351 0.762332383313575 0.939098232445008 0.495829657412097 0.734698073565044 0.854786553397547 0.766021749291286 0.757263163382394 0.528035422450610 1.76412942257156 0.480878637061892 0.779087865119777 0.444875526901451 0.702140015667567 0.475393228042059 0.768312696667354 0.638589873668904 1.27460847277030 1.48937825107580 0.495231950051789 0.493444405935982 0.504732624749794 0.631618660777873 1.27292293987335 0.628781242055882 0.488060785261759 0.554757947749088 1.66634519190679 0.900376834938410 1.10921191147181 1.66756865514712 0.574270568683960 1.70204892135356 0.920784050984837 0.538263886964218 1.76503134659234 0.628734987014323 0.595634664706628 0.509007492420166 0.621106010756416 0.860695885662515 1.58470245060019 0.742921218056206 0.756422431551545 0.640398131277412 1.29779726790267 0.523270699017041];
7 个评论
采纳的回答
dpb
2023-1-16
编辑:dpb
2023-1-16
The log scale plot was helpful to visualize the situation...good idea, but then you're far more aware of what the data are than we can possibly be so not surprising nobody thought to do so...of course, that you didn't attach a data file originally didn't help... :)
Anyways, if the point is to simply adjust the traces so they're all always ordered relative to each other, then how about simply coding in sequence to do so by something like
AdjFactor=100;
cprime=max(c,b.*(1+rand(size(c))/AdjFactor.*b));
The above will pick c whenever it is the larger but then replace c by some pickable scaling factor over the lower for the points that are lower than c. It won't change anything in the original lower (b) nor anything in the upper (c) that is already larger; the failing points will be some randomly perturbed copy of the original b in those locations but always at least some greater than the original b. It does not, however, have any idea that the values between successive points that are replaced are uniformly decreasing -- but, I don't think that is really what you meant to be -- decreasing within the trace; but only that the traces should be ordered at each location amongst the three.
Apply the above sequentially for a and b first, then use the revised b in the second comparison for bprime,c.
If you don't need the original vectors any longer after adjustment, then you can just write c on the LHS above, of course (and similarly for b in the prior comparison).
5 个评论
dpb
2023-1-18
That's what we just did....your mission, should you choose to accept it, is to take the above logic and wrap a function around it if you so choose to use it in that manner.
更多回答(1 个)
Sulaymon Eshkabilov
2023-1-16
编辑:Torsten
2023-1-16
The err in your code is with 0 in b's index that is not acceptable. Because b(0) does not exist. Which element of b vector are you try to call there? E.g 1st element, then b(1)
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