Effacer les filtres
Effacer les filtres

Specifying a position of a Gaussian fit

6 vues (au cours des 30 derniers jours)
TTA
TTA le 21 Déc 2022
Commenté : Image Analyst le 23 Déc 2022
Please how can I specify where my gaussian should be fitted on a data plot
Below is my matlab code and attahed are my sample plot with the data.
clear; clc;
thisfile = pwd + "\matlabdata.txt\";
Txtdata = load(thisfile);
x = Txtdata(:, 1);
y = Txtdata(:, 2);
inc=~isnan(y);
hgt = find(isnan(y));
gEqn = 'a*exp(-((x-b)/c)^2)+d';
startPoints = [5 0 10 2];
[fj,gof,output] = fit(x,y,gEqn,'Start', startPoints, 'Exclude', hgt);%,'Start', startPoints, 'Exclude', [1 10 25]
plot(fj,x,y)
The blue line is anexample of my desired Gaussian.
Its more of shifting a little bit the centre of the Gaussian side ways. I do not know if its possible
Thanks
  1 commentaire
Mathieu NOE
Mathieu NOE le 22 Déc 2022
Seems to me you want to "shape" your gaussian curve , not simply shift it
you have to first define which model would better fullfill your task

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Image Analyst
Image Analyst le 22 Déc 2022
Seems like you blew past the posting guidelines. If you have any more questions, then attach 'matlabdata.txt' with the paperclip icon after you read this:
In the meantime see histfit
rng default; % For reproducibility
r = normrnd(10,1,1000,1);
pd = fitdist(r,'Normal')
pd =
NormalDistribution Normal distribution mu = 9.96737 [9.90538, 10.0294] sigma = 0.998965 [0.957021, 1.04478]
% Construct a histogram with a normal distribution fit.
plotParameters = histfit(r)
plotParameters =
2×1 graphics array: Bar Line
grid on
  5 commentaires
TTA
TTA le 22 Déc 2022
Déplacé(e) : Image Analyst le 23 Déc 2022
Hi @Image Analyst, I have tried but still could not achieve what I want. I just wondering if its possible to achieve the black sketched line in the figure below. I also attached my code.
thanks
Image Analyst
Image Analyst le 23 Déc 2022
What makes you think the black curve is better or more accurate than the red curve?
Maybe you should fit a different formula, like a Rayleigh or something. What is the theory behind this experiment? What formula does theory say your data should follow? In the middle range it just seems like total noise - a shotgun blast - like it follows no theoretical distribution whatsoever.

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