How to remove the value using Histogram

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Med Future
Med Future on 24 Jan 2023
Commented: Star Strider on 27 Jan 2023
I have the following data, in which my original value is 15 which have count of 7360
I want to remove the remaining values which count less then 33% of orginal values or multiple of the original value
for example in this case I have 30,45 ,60,75 and 90 I want to remove this values. and value of 1 also
How can i do that in MATLAB

Answers (1)

Star Strider
Star Strider on 24 Jan 2023
I have only a vague idea of what you want to do, especially since the .mat file does not appear to contain the same data as depicted in the posted plot image.
Try this —
LD = load(websave('histogram','https://www.mathworks.com/matlabcentral/answers/uploaded_files/1272595/histogram.mat'))
LD = struct with fields:
ans: [8839×1 double]
v = LD.ans;
Ev = linspace(0, 100, 101)
Ev = 1×101
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29
figure
hh = histogram(v, Ev);
Vals = hh.Values;
Edgs = hh.BinEdges;
Retain = (Vals > max(Vals)/3);
Out = Vals(Retain)
Out = 1×2
2896 4568
OutBinsLowerEdge = Edgs(Retain)
OutBinsLowerEdge = 1×2
14 15
If you want to remove the associated data in the original file corresponding to those values, that would be relatively straightforward using logical indexing. Another approach would be to use histcounts, return the 'Bins' output, and index into that.
.
  21 Comments
Star Strider
Star Strider on 27 Jan 2023
You need to force ‘Data’ to be a column vector to work with my code, using the ‘(:)’ operator:
Data=NewData(:);
I decided to do this to make my code compatible with all the data sets, since some are row vectors and some are coliumn vectors.
Try this —
LD = load(websave('secondata','https://www.mathworks.com/matlabcentral/answers/uploaded_files/1275815/secondata.mat'));
Data = LD.NewData(:) % Force Column Vector
Data = 16075×1
100.0000 100.0000 100.0000 100.0000 100.0000 100.0000 100.0000 100.0000 100.0000 100.0000
h=histogram(Data,10000,"BinMethod","sturges",'BinWidth',1,'BinLimits',[1 10000]);
[N,Edges,Bin] = histcounts(Data,10000,"BinMethod","sturges",'BinWidth',1,'BinLimits',[1 10000]);
Retain = N > max(N)/3; % Retain Values In Bins Greater Than One-Third Of The Meximum Bin Count Value
FindBins = find(Retain)
FindBins = 1×5
99 100 150 200 250
RetainDataLv = (Bin == FindBins); % Values In 'Bin' Corresponding To 'Retain' Test
SzRD = size(RetainDataLv);
[~,idx] = min(SzRD);
RetainData = Data(any(RetainDataLv,idx)) % Return Desired Subset OF 'Data'
RetainData = 10289×1
100.0000 100.0000 100.0000 100.0000 100.0000 100.0000 100.0000 100.0000 100.0000 100.0000
That should work with all the data vectors, regardless of whether their initial orientation is as row or column vectors.
.

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