down sampling a vector keeping the order of elements values
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I have a vector of u with size of 45X1.
I want to down sampling it to u_new with size of 7X1. u here is the velocity profile in a tube. So I'd like to have 7 points of them instead of 45 points to plot it with 7 points. when moving from the start and end points of the vector toward the center, data values are increasing. I need to keep this order for 7 bins as well.
what would be the best way to do this?
u=
0.0182
0.0253
0.0324
0.0404
0.0487
0.0552
0.0603
0.0643
0.0674
0.0699
0.0718
0.0732
0.0743
0.0751
0.0757
0.0762
0.0767
0.0770
0.0772
0.0773
0.0774
0.0775
0.0775
0.0775
0.0774
0.0772
0.0770
0.0767
0.0764
0.0760
0.0755
0.0749
0.0740
0.0730
0.0716
0.0699
0.0676
0.0646
0.0608
0.0559
0.0495
0.0416
0.0341
0.0273
0.0199
0 comentarios
Respuestas (1)
Voss
el 17 de Jul. de 2023
u=[
0.0182
0.0253
0.0324
0.0404
0.0487
0.0552
0.0603
0.0643
0.0674
0.0699
0.0718
0.0732
0.0743
0.0751
0.0757
0.0762
0.0767
0.0770
0.0772
0.0773
0.0774
0.0775
0.0775
0.0775
0.0774
0.0772
0.0770
0.0767
0.0764
0.0760
0.0755
0.0749
0.0740
0.0730
0.0716
0.0699
0.0676
0.0646
0.0608
0.0559
0.0495
0.0416
0.0341
0.0273
0.0199
];
x = 1:numel(u);
plot(x,u)
hold on
x_new = linspace(x(1),x(end),7);
u_new = interp1(x,u,x_new);
plot(x_new,u_new,'-o')
4 comentarios
Voss
el 17 de Jul. de 2023
u=[
0.0182
0.0253
0.0324
0.0404
0.0487
0.0552
0.0603
0.0643
0.0674
0.0699
0.0718
0.0732
0.0743
0.0751
0.0757
0.0762
0.0767
0.0770
0.0772
0.0773
0.0774
0.0775
0.0775
0.0775
0.0774
0.0772
0.0770
0.0767
0.0764
0.0760
0.0755
0.0749
0.0740
0.0730
0.0716
0.0699
0.0676
0.0646
0.0608
0.0559
0.0495
0.0416
0.0341
0.0273
0.0199
];
x = 1:numel(u);
plot(x,u)
hold on
nu = numel(u);
npts = 7;
idx = (1:npts:nu)+(0:npts-1).'
% in case nu is not a multiple of npts (e.g., 45 is not a multiple of 7),
% add some NaNs to the end of u, which will not affect the average except
% the last bin will have fewer non-NaN points than the other bins
if idx(end) > nu
bad_idx = idx > nu;
u_temp = [u; NaN(idx(end)-nu,1)];
else
bad_idx = [];
u_temp = u;
end
% average value of each group of npts values:
u_new = mean(u_temp(idx),1,'omitnan')
% average index of each group of npts values:
idx(bad_idx) = NaN;
x_new = mean(idx,1,'omitnan')
plot(x_new,u_new,'-o')
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