Finding the Y value corresponding to X value in a parametric plot
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how do i find the value of Y when X= 1360 for every loop? X and Y gives a matrix. s is a random number for each loop
smax=0.4;
smin=0.1;
S=smin+rand(1,n)*(smax-smin);
S_cumulative=cumsum(S);
n= 3200;
R=200.04;
V=30;
v=20000;
i=1;
while i<n
t= linspace (0,50,100000);
X=R*sind((v*t/(R))+(V*(t))+S_cumulative(i);
Y=-R*cosd((v*t/R));
end
i=i+1;
2 件のコメント
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KL
2017 年 10 月 5 日
編集済み: KL
2017 年 10 月 5 日
If all your curves have the same timestep and same number of measurements why not put them all together in a matrix and then find the minimum? For example,
allY = rand(10,4); %say, you have 4 curves with 10 values (random data in this example)
X = (1:10)';
data_summary = [X allY] %put them together
intvl = 3:6; %in your case 1360 to 1400
minVals = min(data_summary(intvl,2:end))
7 件のコメント
KL
2017 年 10 月 11 日
As far as I have understood, every column of the Y matrix is a curve and they are somehow associated with X along the row. So when I say find minimum value of Y when X = 6 (for example), we are trying to find the minimum along the row 6 across all columns (hence along all curves).
You're saying there are 100000 values of y for each iteration (or curve), well that means 100000 x, right? How about storing it vertically?
X Y1
1 0.4
2 0.2
... ...
100000 0.34
and then concatenate it horizontally for the next iteration,
X Y1 Y2
1 0.4 0.8
2 0.2 0.4
... ... ...
100000 0.34 0.1
So, this how I could understand the problem from your descriptions. This is why we insist on creating a minimal example with a sample code.
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