Abrupt changes in data; 'ischange', means, and 'stairs'

Question

noMathWiz 2020-7-10

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此问题的直接链接

https://ww2.mathworks.cn/matlabcentral/answers/562640-abrupt-changes-in-data-ischange-means-and-stairs

编辑： Matt J 2020-7-10

dataTest.mat

I have multiple, large data sets that I need to acertain the means along certain linear regions. The regions are dileneated by relatively abrupt changes, as shown in the figure below that I manually marked up.

Ultimately I want a 7 element array of the means of each of these regions (aka the y-intercepts as shown). Thoughts? Other types of curve fittings that could help?

A sample data set is attached.

The below code not working for me, and I've tried a variety of max number of changes and threshold levels.

[TF,S1] = ischange(a, 'linear','MaxNumChanges',12);
plot(a, '*')
hold on
stairs(S1)

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Answer 1

Matt J 2020-7-10

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https://ww2.mathworks.cn/matlabcentral/answers/562640-abrupt-changes-in-data-ischange-means-and-stairs#answer_464219

编辑：Matt J 2020-7-10

在 MATLAB Online 中打开

Here is a method using the Image Processing Toolbox (treating the signal as a 1D image, in other words).

load('dataTest.mat')
w=5000;
b=movmedian(a,w);
cmax=movmax(b,[w,1]);
cmin=movmin(b,[1,w]);
q=cmax-cmin;
lmap=bwlabel(q<0.01);
result = regionprops('table',lmap,b,'MeanIntensity')
plot(a)
hold on
for i=1:size(result,1)
   plot(xlim,result{i,1}*[1,1],'--') 
end
hold off

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Answer 2

Matt J 2020-7-10

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此回答的直接链接

https://ww2.mathworks.cn/matlabcentral/answers/562640-abrupt-changes-in-data-ischange-means-and-stairs#answer_463787

编辑：Matt J 2020-7-10

Yes, you can use splitapply(@mean,data,G)

https://www.mathworks.com/help/matlab/ref/splitapply.html

with G identifying the regions you wwant grouped together,

https://www.mathworks.com/help/matlab/ref/splitapply.html#butc687-G

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noMathWiz 2020-7-10

Thanks for your help, but I think this is too much manual work (i.e. it requires me to manually find where the changes in data are, delete the data, then create G vectors of the same length).

Matt J 2020-7-10

编辑：Matt J 2020-7-10

在 MATLAB Online 中打开

If you were to construct G manually, it would indeed be a lot of work, but the philosophy behind splitapply is that you would find some automated way to construct the group labels. Below is a method that makes use of group1s from the File Exchange.

https://www.mathworks.com/matlabcentral/fileexchange/78008-group1s

load('dataTest.mat')
w=5000;
b=movmedian(a,w);
cmax=movmax(b,[w,1]);
cmin=movmin(b,[1,w]);
q=cmax-cmin;
lmap=group1s(q<0.01);
result = splitapply(@mean, b, findgroups(lmap))
 result(1)=[];
plot(a)
hold on
for i=1:size(result,1)
   plot(xlim,result(i)*[1,1],'--') 
end
hold off