- You take the mean and max of the U values; I believe you want the I values instead.
- You have plenty of data points, so the default linear interpolation will follow the trend better.
- Some data at the end will have to be excluded from the mean curve. You could use the 'omitnan' flag, but that will cause a discontinuity in the curve.
I'm having a problem averaging multiple curves using interp1
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Philip Krämer
el 11 de Mzo. de 2023
Comentada: Chris
el 12 de Mzo. de 2023
Hi everyone.
I have multiple polarisation curves that I want to display the averge of. I tried using linspace to create a base vector and interpolating using interp1. Unfortunately that hasn't properly worked for me and I was hoping someone might be able to help.
Thank you in advance!
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Respuesta aceptada
Chris
el 11 de Mzo. de 2023
Editada: Chris
el 11 de Mzo. de 2023
% Mittelung mehrerer Messungen
clearvars
[filenames, pathname] = uigetfile('MultiSelect', 'on', '*.*');
fullname = fullfile(pathname,filenames);
clear savename
for z = 1:length(fullname)
load (fullname{1,z})
loadDaten{1,z} = Daten;
end
Daten = loadDaten;
IVC_mean = cell (3,length(fullname));
var = zeros(1,length(fullname));
Names = string(var);
Imax = zeros (1,length(fullname));
Umax = zeros (1,length(fullname));
Umin = zeros (1,length(fullname));
for z = 1:length(fullname)
% Messdaten
Ewe = Daten{1,z}(:,7);
I = Daten{1,z}(:,8).*1000;
Ismooth = smoothdata(I,'sgolay');
% Details der Messung
savename{1,z} = extractBefore(filenames{1,z},".");
Names(z) = savename {1,z};
% sortieren
IVC_mean{1,z} = Ewe;
IVC_mean{2,z} = abs(Ismooth);
IVC_mean{3,z} = extractAfter(strrep(savename{1,z},'_',' '),' ');
% % outlier
% pp = isoutlier(IVC_mean{2,z});
% ind = find(pp);
% IVC_mean{4,z} = ind;
% IVC_mean{5,z} = IVC_mean{2,z};
% IVC_mean{5,z}(ind) = NaN;
% einzeln plot
h = scatter(IVC_mean{2,z},IVC_mean{1,z});
xlabel(['I']);ylabel(['U']);
hold on
% Grenzen für xq
% IVC_mean{6,z} = min(IVC_mean{1,z});
% IVC_mean{7,z} = max(IVC_mean{1,z});
IVC_mean{6,z} = min(IVC_mean{2,z});
IVC_mean{7,z} = max(IVC_mean{2,z});
Umin(z) = IVC_mean{6,z};
Umax(z) = IVC_mean{7,z};
end
%
% Interpolation
Umin = min(Umin);
Umax = max(Umax);
% vorgegebener Bezugsvektor
UC = linspace(Umin,Umax,10000);
for z = 1:length(fullname)
% IVC_mean{8,z} = interp1(IVC_mean{2,z},IVC_mean{1,z},UC,'spline');
IVC_mean{8,z} = interp1(IVC_mean{2,z},IVC_mean{1,z},UC,'linear');
h2 = plot(UC,IVC_mean{8,z},'k--','LineWidth',2);
hold on
end
mfit = mean(cat(1,IVC_mean{8,:}));
plot(UC, mfit,'m','LineWidth',2);
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Más respuestas (1)
Walter Roberson
el 11 de Mzo. de 2023
h = scatter(IVC_mean{2,z},IVC_mean{1,z});
So {1} is used as y values and {2} is used as x values.
IVC_mean{6,z} = min(IVC_mean{1,z});
IVC_mean{7,z} = max(IVC_mean{1,z});
min and max of the y values.
Umin = min(Umin);
Umax = max(Umax);
UC = linspace(Umin,Umax,10000);
smallest y and greatest y
IVC_mean{8,z} = interp1(IVC_mean{2,z},IVC_mean{1,z},UC,'spline');
you pass in known x values and corresponding known y values and you query based on UC, which is based on y values, not on x values.
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