Getting statistics from a struct

4 vues (au cours des 30 derniers jours)
Merijn
Merijn le 29 Juil 2014
Réponse apportée : Merijn le 31 Juil 2014
I have a struct with experimental data in it. The fields in the struct are input settings and measurement results. For all possible input settings I have a lot of measurement results, but they are not organized.
So for example I have myResults(1).Inputsetting1 and myResults(1).Inputsetting2 as input settings for one experiment and myResults(1).MeasurementA and myResults(1).MeasurementB as measurement results for that experiment. And then there exist e.g. 20 different elements which all share the same input settings. Now I want to know what the mean, standard deviation, etc. is of myResults.MeasurementA and myResults.MeasurementB of all the elements which share the same inputsettings. Is there a simple way to do this?
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dpb
dpb le 29 Juil 2014
Think need some more detail to have specifics, but -- look at
doc structfun
and the section on structures with addressing them, particularly the portion to create hyper-rectangles or arrays of similar data types from various fields. Then you can process those arrays selected by various characteristics.
If you have the Statistics Toolbox, look at
doc struc2dataset
Patrik Ek
Patrik Ek le 30 Juil 2014
Try to parse the struct with functions like fieldnames and so. If you know where to find the data, a recursive parser might do the trick.

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Sara
Sara le 29 Juil 2014
If measurementA is just one value, you can do:
mean([myResults.MeasurementA])
Try it for your data and look at the results and adjust.
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Merijn
Merijn le 30 Juil 2014
Modifié(e) : Merijn le 30 Juil 2014
This is a subset of the data. Two input parameters are Droplet_spacing and Gap, and an output is e.g. Area.
Sara
Sara le 30 Juil 2014
Look at the code below. Is that what you are looking for?
clc
clearvars
close all
load('partofresult.mat');
drop_array = [partofresult.Droplet_spacing];
gap_array = [partofresult.Gap];
area_array = [partofresult.Area];
drop = unique([partofresult.Droplet_spacing]);
gap = unique([partofresult.Gap]);
averages = zeros(numel(drop),numel(gap));
for i = 1:numel(drop)
for j = 1:numel(gap)
k = find(drop_array == drop(i) & gap_array == gap(j));
averages(i,j) = mean(area_array(k));
end
end
[drop,gap] = meshgrid(drop,gap);
plot3(drop,gap,averages','o')

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Merijn
Merijn le 31 Juil 2014
Wow, that's exactly what I need. Thanks a million!

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