Exclude the NaN, 0, empty and Inf values from the analysis.
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Luccas S.
il 11 Feb 2022
Commentato: Luccas S.
il 11 Feb 2022
When calculating the PE value, I would like it not to calculate when Ia_future = 0, Nan or Inf.
I believe the way I did it, it's still calculating. Because some PE values are still Inf.
Or if there was some way to exclude those values. The problem is that I need to plot a (t,PE) graphic and if I exclude some PE values the two will have different dimensions and I will not be able to analyze the graph..
for n = 4:size(t,1)
X = [Ia(n-1,1) Ia(n-2,1) ; Ia(n-2,1) Ia(n-3,1)];
future = [Ia(n,1) ; Ia(n-1,1)];
C = X\future;
Ia_future(n,1) = C(1,1)*Ia(n,1)+C(2,1)*Ia(n-1,1);
if (isnan(Ia_future(n, 1)) || isinf(Ia_future(n,1) || isempty(Ia_future(n,1) || Ia_future(n,1)==0))) %|| %(isnan(p(n, 1)) || p(n, 1) == 0)
continue
end
PE(n,1)=(Ia(n,1)+Ia_future(n,1))/(2000/5);
end
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Benjamin Thompson
il 11 Feb 2022
Vectorize your calculations using index vectors. For example:
>> Test = [0 1 inf NaN]
Test =
0 1 Inf NaN
>> Inan = isnan(Test)
Inan =
1×4 logical array
0 0 0 1
>> Iinf = isinf(Test)
Iinf =
1×4 logical array
0 0 1 0
>> Igood = ~isinf(Test) & ~isnan(Test)
Igood =
1×4 logical array
1 1 0 0
Then you can calculate PE as a function of Ia_future outside the for loop, something like:
PE(Igood,1)=(Ia(Igood,1)+Ia_future(Igood,1))/(2000/5);
Only the rows of PE corresponding to where Igood is one will be updated. You may need to calculate the index vector looking at both Ia and Ia_future if they can have bad values in different spots.
2 Commenti
Benjamin Thompson
il 11 Feb 2022
Yes you can define an index vector using any kind of comparison test.
Più risposte (1)
Image Analyst
il 11 Feb 2022
Is this helpful:
data = [0, 9, inf, NaN, 42];
mask = (data ~= 0) & isfinite(data)
extractedData = data(mask)
Using isfinite() takes the place/combines both ~isinf() and ~isnan() all into one simple function.
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